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Journal articles on the topic 'Corporations – Growth – Mathematical models'

Author: Grafiati

Published: 10 March 2023

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1

Sofiko Dzhvarsheishvili, Sofiko Dzhvarsheishvili. "Developing Tendencies of Foreign Direct Investments." New Economist 16, no. 03 (January 28, 2022): 41–47. http://dx.doi.org/10.36962/nec62-6303-042021-41.

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The formation process of foreign direct investment theories began in the second half of the twentieth century, it was period that the importance of foreign direct investment (FDI) in international capital movement increased, and this period was characterized by the rapid growth of multinational enterprises, which, in turn, is a major source of FDI. Scientific and technological progress has become the basis for decisions of transnational companies to invest capital in to different parts of the world and to coordinate and control its many branches from one country. During this period scientists actively began to develop theories and mathematical models of foreign direct investments, which meant studying the genesis of FDI, as well as analyzing their impact on the economy of both the host and the issuer countries. The analysis of foreign direct investment theories is closely related to the study of the activities of multinational corporations. Among them are some paradigms containing interesting discoveries that view the foreign direct investment as a contributing factor for the economic growth of the host country and its industrial development. In this article, the author consider the key theories and models of foreign direct investment: 1. The Product Life Cycle Theory, which is developed by Vernon; 2. Transnational companies and monopolistic computation theory, which is developed by Hymer; 3. Generalized theory of economic development, which is developed by Akamatsu; 4.The competitive advantage of nation’s theory, which is developed by Porter; 5.The eclectic paradigm and country's investment development theory, which is developed by Dunning; 6.Differential model of capital distribution between countries by Leontiev. The theories and models provide a basic range of motives of investors' behavior in the world market. It also makes it possible to analyze what impact foreign direct investment has on the economy of the recipient country and what forecast the recipient country's economy will have. Keywords: Globalization, Foreign Direct Investment, Economic Growth, Multinational Corporations, Analysis of Basic Theories and models;

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Du, Yuping, Rongping Kang, and Yinbin Ke. "Understanding the growth models of Chinese multinational corporations." International Journal of Chinese Culture and Management 1, no. 4 (2008): 451. http://dx.doi.org/10.1504/ijccm.2008.020010.

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3

Et.al, D. Priyadarshini. "A Novel Technique for IDS in Distributed Data Environment Using Merkel Based Security Mechanism for Secure User Allocation." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 3 (April 11, 2021): 4284–97. http://dx.doi.org/10.17762/turcomat.v12i3.1720.

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Multiple corporations and people frequently launching their data in the cloud environment. With the huge growth of data mining and the cloud storage paradigm without checking protection policies and procedures that can pose a great risk to their sector. The data backup in the cloud storage would not only be problematic for the cloud user but also the Cloud Service Provider (CSP). The unencrypted handling of confidential data is likely to make access simpler for unauthorized individuals and also by the CSP. Normal encryption algorithms need more primitive computing, space and costs for storage. It is also of utmost importance to secure cloud data with limited measurement and storage capacity. Till now, different methods and frameworks to maintain a degree of protection that meets the requirements of modern life have been created. Within those systems, Intrusion Detection Systems (IDS) appear to find suspicious actions or events which are vulnerable to a system's proper activity. Today, because of the intermittent rise in network traffic, the IDS face problems for detecting attacks in broad streams of links. In existing the Two-Stage Ensemble Classifier for IDS (TSE-IDS) had been implemented. For detecting trends on big data, the irrelevant data characteristics appear to decrease both the velocity of attack detection and accuracy. The computing resource available for training and testing of the IDS models is also increased. We have put forward a novel strategy in this research paper to the above issues to improve the balance of the server load effectively with protected user allocation to a server, and thereby minimize resource complexity on the cloud data storage device, by integrating the Authentication based User-Allocation with Merkle based Hashing-Tree (AUA-MHT) technique. Through this, the authentication attack and flood attack are detected and restrict unauthorized users. By this proposed model the cloud server verifies, by resolving such attacks, that only approved users are accessing the cloud info. The proposed framework AUA-MHT performs better than the existing model TSE-IDS for parameters such as User Allocation Rate, Intrusion Detection Rate and Space Complexity

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Dolganova, Olga, and Valeriy Lokhov. "Mathematical models of growth deformation." PNRPU Mechanics Bulletin 1 (March 30, 2014): 126–41. http://dx.doi.org/10.15593/2224-9893/2014.1.06.

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Pavankumari, V. "Mathematical and Stochastic Growth Models." International Journal for Research in Applied Science and Engineering Technology 9, no. 11 (November 30, 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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Kumari, V. Pavan, Venkataramana Musala, and M. Bhupathi Naidu. "Mathematical and Stochastic Growth Models." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (May 31, 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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7

Oliveri, Hadrien, and Alain Goriely. "Mathematical models of neuronal growth." Biomechanics and Modeling in Mechanobiology 21, no. 1 (January 7, 2022): 89–118. http://dx.doi.org/10.1007/s10237-021-01539-0.

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AbstractThe establishment of a functioning neuronal network is a crucial step in neural development. During this process, neurons extend neurites—axons and dendrites—to meet other neurons and interconnect. Therefore, these neurites need to migrate, grow, branch and find the correct path to their target by processing sensory cues from their environment. These processes rely on many coupled biophysical effects including elasticity, viscosity, growth, active forces, chemical signaling, adhesion and cellular transport. Mathematical models offer a direct way to test hypotheses and understand the underlying mechanisms responsible for neuron development. Here, we critically review the main models of neurite growth and morphogenesis from a mathematical viewpoint. We present different models for growth, guidance and morphogenesis, with a particular emphasis on mechanics and mechanisms, and on simple mathematical models that can be partially treated analytically.

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8

Leung, C. H. C. "Mathematical Models of Fire Growth." Computer Journal 28, no. 2 (February 1, 1985): 179–83. http://dx.doi.org/10.1093/comjnl/28.2.179.

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9

Dmitriev, V. I., E. S. Kurkina, and O. E. Simakova. "Mathematical models of urban growth." Computational Mathematics and Modeling 22, no. 1 (January 2011): 54–68. http://dx.doi.org/10.1007/s10598-011-9088-8.

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10

Suzuki, Takashi. "Mathematical models of tumor growth systems." Mathematica Bohemica 137, no. 2 (2012): 201–18. http://dx.doi.org/10.21136/mb.2012.142866.

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11

Roose, Tiina, S. Jonathan Chapman, and Philip K. Maini. "Mathematical Models of Avascular Tumor Growth." SIAM Review 49, no. 2 (January 2007): 179–208. http://dx.doi.org/10.1137/s0036144504446291.

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12

Buschang, P. H., R. Tanguay, L. LaPalme, and A. Demirjian. "Mandibular growth prediction: mean growth increments versus mathematical models." European Journal of Orthodontics 12, no. 3 (August 1, 1990): 290–96. http://dx.doi.org/10.1093/ejo/12.3.290.

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13

Pletnev, D. A., and E. V. Kozlova. "Institutional Structure of Corporations (the Case of Large Russian Corporations)." Zhurnal Economicheskoj Teorii 18, no. 1 (2021): 87–102. http://dx.doi.org/10.31063/2073-6517/2021.18-1.6.

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Modern corporation is the main form of social production but its activity and development still remain a largely underex-plored question. Economic theory predominantly relies on simplified approaches based on mathematical modeling to study cor-porations, which gives us little in-depth understanding of the essence of this phenomenon. On the other hand, applied research on corporations does not rise to the level of theoretically significant generalizations. This paper proposes the following theoretical and methodological approach to analyzing the phenomenon of corporation: we identify institutional elements in its structure — institutions and routines. We use the cases of the largest Russian companies to demonstrate the possibility of using this approach to analyze specific corporations and their aggregates. The paper describes nine different routines that structure corporate actors’ interactions based on social norms, coercion, and private interest. We also conduct an analysis of the official sites of Russian cor-porations and sites with employee feedback, which leads us to evaluating each institution’s development from the triad: coercion, assistance, and opportunism. We found a significant correlation between the development level of each corporation’s institutional structure and its efficiency (profitability of sales and capitalization growth rate).

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Cannell, Melvin G. R. "Mathematical Models of Crop Growth and Yield." Crop Science 43, no. 3 (May 2003): 1145. http://dx.doi.org/10.2135/cropsci2003.1145.

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15

Buschini, MLT, MAP Abuabara, and Miguel Petrere-Jr. "Mathematical models for Isoptera (Insecta) mound growth." Brazilian Journal of Biology 68, no. 3 (August 2008): 529–33. http://dx.doi.org/10.1590/s1519-69842008000300009.

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In this research we proposed two mathematical models for Isoptera mound growth derived from the Von Bertalanffy growth curve, one appropriated for Nasutitermes coxipoensis, and a more general formulation. The mean height and the mean diameter of ten small colonies were measured each month for twelve months, from April, 1995 to April, 1996. Through these data, the monthly volumes were calculated for each of them. Then the growth in height and in volume was estimated and the models proposed.

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16

Kondratyev, A., A. Lyaptsev, and I. Mikhailova. "Mathematical modeling of thrombus growth phenomenological models." Applied Mathematical Sciences 9 (2015): 429–46. http://dx.doi.org/10.12988/ams.2015.49771.

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17

FUJIKAWA, Hiroshi, and Takeshi ITOH. "Mathematical Models for Microbial Growth in Foods." Japanese Journal of Food Microbiology 12, no. 4 (1996): 203–8. http://dx.doi.org/10.5803/jsfm.12.203.

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18

Villacorta, José A., Jorge Castro, Pilar Negredo, and Carlos Avendaño. "Mathematical foundations of the dendritic growth models." Journal of Mathematical Biology 55, no. 5-6 (July 24, 2007): 817–59. http://dx.doi.org/10.1007/s00285-007-0113-7.

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19

Werhane, Patricia H. "Exporting Mental Models: Global Capitalism in the 21st Century." Business Ethics Quarterly 10, no. 1 (January 2000): 353–62. http://dx.doi.org/10.2307/3857720.

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Abstract:The most serious ethical challenge facing multinational corporations in the next century is their exportation of the mental model of Western-style capitalism. This model promises that industrialized free enterprise in a free trade global economy, where businesses and entrepreneurs can pursue their interests competitively without undue regulations or labor restrictions, will produce growth and well-being, i.e., economic good, in every country or community where this phenomenon is allowed to operate. This paper points to some limitations to this model and illustrates how multinational corporations might meet this challenge.

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20

Lampl, Michelle. "Perspectives on modelling human growth: Mathematical models and growth biology." Annals of Human Biology 39, no. 5 (July 27, 2012): 342–51. http://dx.doi.org/10.3109/03014460.2012.704072.

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21

Feinberg, Alec. "Accelerated Reliability Growth Models." Journal of the IEST 37, no. 1 (January 1, 1994): 17–23. http://dx.doi.org/10.17764/jiet.2.37.1.f2u73m8022207868.

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Extending reliability growth so that it can be applied in the area of accelerated testing will enable one to apply all the reliability growth tools and their planning advantages. This paper describes linking these two areas together into what is termed accelerated reliability growth testing (ARGT). Mathematical equations of ARGT for both iso-stress and step-stress accelerated testing are modelled.

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22

Bhattachar, Atanu, and B. Satheesan. "Applications of Mathematical Models on Tumor Growth Rate." Asian Journal of Mathematics & Statistics 7, no. 2 (June 15, 2014): 60–67. http://dx.doi.org/10.3923/ajms.2014.60.67.

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23

Mugarab-Samedi, Fuad, Sahbaz Heir, and Saboor Ul-Bashar. "Mathematical Models and their Application in Cancer Growth." Acta Scientific Cancer Biology 5, no. 7 (June 30, 2021): 35–41. http://dx.doi.org/10.31080/ascb.2021.04.0323.

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24

MARUŠIĆ, M., and S. VUK-PAVLOVIĆ. "PREDICTION POWER OF MATHEMATICAL MODELS FOR TUMOR GROWTH." Journal of Biological Systems 01, no. 01 (March 1993): 69–78. http://dx.doi.org/10.1142/s0218339093000069.

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We compared the Gompertz model, the generalized Gompertz model, the Piantadosi model, the autostimulation model and the polynomials for the power to predict growth of multicellular tumor spheroids as paradigms of the prevascular phase of tumor growth. For the comparison of models we developed a criterion that established the Gompertz model as the model with the best prediction power. The prediction power of the remaining models was ranked in declining order: the generalized Gompertz model; the mutually indistinguishable Piantadosi model and the autostimulation model; and the polynomials. The ranking of models was not affected by the applied minimization criteria of weighted least squares, unweighted least squares and fitting to logarithmically transformed data, but the prediction power was affected by these criteria. The best predictions were obtained by weighted least squares, closely followed by fitting to logarithmically transformed data. The unweighted least-squares minimization was much less applicable for prediction (and description) of growth.

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25

Ledford, A. W., and T. J. Cole. "Mathematical models of growth in stature throughout childhood." Annals of Human Biology 25, no. 2 (January 1998): 101–15. http://dx.doi.org/10.1080/03014469800005482.

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26

Lei, Jinzhi, Wing-Cheong Lo, and Qing Nie. "Mathematical models of morphogen dynamics and growth control." Annals of Mathematical Sciences and Applications 1, no. 2 (2016): 427–71. http://dx.doi.org/10.4310/amsa.2016.v1.n2.a6.

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27

López, S., M. Prieto, J. Dijkstra, M. S. Dhanoa, and J. France. "Statistical evaluation of mathematical models for microbial growth." International Journal of Food Microbiology 96, no. 3 (November 2004): 289–300. http://dx.doi.org/10.1016/j.ijfoodmicro.2004.03.026.

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28

Babák, Libor, P. Šupinová, and R. Burdychová. "Growth models of Thermus aquaticus and Thermus scotoductus." Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis 60, no. 5 (2012): 19–26. http://dx.doi.org/10.11118/actaun201260050019.

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Members of the genusThermusfamily are signifiant producers of secondary metabolites, which are very commonly used in industry. Beside the productivity, it is also important to study the growth curve of each strain. The mathematical models which are commonly used to describe behavior of microbial strains under different physical and chemical conditions can reduce measured data.In this study, the mathematical models which describe only the microbial count were used. For analysis, Verhulst model, von Bertalanffy model and Richards model were chosen.During the cultivation ofThermus aquaticusandThermus scotoductusstrains, optical density (OD) and concentration of microorganisms were measured. The mathematical models were fitted to experimental data. The fitting was made in program MATLAB. The coefficients of models and statistical evaluation of goodness of fit of models were identified from mathematical analyses. Graphs of individual models were plotted with prediction bounds. 95% confidence levels were used to statistical evaluation of goodness of fit of models and prediction bounds.Richards model was evaluted as the most corresponding with experimental data. Similar results were reached using Verhulst model. Von Bertalanffy model was not coresponded with experimental data.

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Piccone, Ashley. "Improving urban growth models with fractional calculus." Scilight 2022, no. 43 (October 21, 2022): 431105. http://dx.doi.org/10.1063/10.0014416.

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Nuryaman, A., M. A. A. F. Pamungkas, and S. Saidi. "Forecasting of Bandar Lampung’s Population Using Growth Mathematical Models." Journal of Physics: Conference Series 1338 (October 2019): 012037. http://dx.doi.org/10.1088/1742-6596/1338/1/012037.

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31

Chowell, Gerardo, Lisa Sattenspiel, Shweta Bansal, and Cécile Viboud. "Mathematical models to characterize early epidemic growth: A review." Physics of Life Reviews 18 (September 2016): 66–97. http://dx.doi.org/10.1016/j.plrev.2016.07.005.

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32

KING, J. R., and S. J. FRANKS. "Mathematical analysis of some multi-dimensional tissue-growth models." European Journal of Applied Mathematics 15, no. 3 (June 2004): 273–95. http://dx.doi.org/10.1017/s0956792504005480.

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Mathematical models for the growth of nutrient-rich tissue are presented and a number of properties of the resulting models outlined. The focus is on obtaining broadly applicable results for the simplest appropriate formulations by using matched-asymptotic, moving-boundary and thin-film approaches; the relevance of the results to a variety of specific biological applications will be addressed elsewhere, as will the inclusion of additional physical phenomena.

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Racsko, P., and M. Semenov. "Analysis of mathematical principles in crop growth simulation models." Ecological Modelling 47, no. 3-4 (September 1989): 291–302. http://dx.doi.org/10.1016/0304-3800(89)90007-0.

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34

Booker, B. M., A. Forrest, and P. F. Smith. "Novel mathematical models for bacterial growth, death & pharmacodynamics." Clinical Pharmacology & Therapeutics 73, no. 2 (February 2003): P24. http://dx.doi.org/10.1016/s0009-9236(03)90442-5.

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35

Ortega, J. K. E., and S. W. J. Welch. "Mathematical Models for Expansive Growth of Cells with Walls." Mathematical Modelling of Natural Phenomena 8, no. 4 (2013): 35–61. http://dx.doi.org/10.1051/mmnp/20138404.

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36

Mantzaris, Nikolaos V., Prodromos Daoutidis, Friedrich Srienc, and Arnold G. Fredrickson. "Growth processes in a cascade of bioreactors: Mathematical models." AIChE Journal 45, no. 1 (January 1999): 164–76. http://dx.doi.org/10.1002/aic.690450114.

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37

Heaps, Aaron J., Tyler D. Dawson, Jace C. Briggs, Megan A. Hansen, and Jamie L. Jensen. "Deriving Population Growth Models by Growing Fruit Fly Colonies." American Biology Teacher 78, no. 3 (March 1, 2016): 221–25. http://dx.doi.org/10.1525/abt.2016.78.3.221.

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Population growth presents a unique opportunity to make the connection between mathematical and biological reasoning. The objective of this article is to introduce a method of teaching population growth that allows students to utilize mathematical reasoning to derive population growth models from authentic populations through active learning and firsthand experiences. To accomplish this, we designed a lab in which students grow and count populations of Drosophila over the course of 12 weeks, modifying abiotic and biotic limiting factors. Using the data, students derive exponential and logistic growth equations, through mathematical reasoning patterns that allow them to understand the purpose of these models, and hypothesize relationships between various factors and population growth. We gathered student attitudinal data and found that students perceived the lab as more effective, better at preparing them for lecture, and more engaging than the previous lab used. Through this active and inquiry-based method of teaching, students are more involved and engaged in both mathematical and biological reasoning processes.

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Karev, Georgy P., and Faina S. Berezovskaya. "Mathematical Models for the "Schmalhgausent Law"." Journal of Biological Systems 11, no. 03 (September 2003): 245–64. http://dx.doi.org/10.1142/s0218339003000865.

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The empirical formula of growth (decrease) of biological objects, corresponding to polygonal line in double logarithmic scale is discussed. Leslie-type models producing solutions similar to some specific cases of this formula (including logistic-like curve) are studied. As an example, a model of tree stand self-thinning is derived and investigated; the model allows fitting empirical data with high accuracy.

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Skufina, Tatiana, Ekaterina Bazhutova, Vera Samarina, and Natalia Serova. "CORPORATE SOCIAL RESPONSIBILITY AS A RESERVE FOR THE GROWTH OF ENTREPRENEURIAL ACTIVITY IN THE RUSSIAN ARCTIC." Humanities & Social Sciences Reviews 7, no. 6 (December 25, 2019): 1024–31. http://dx.doi.org/10.18510/hssr.2019.76151.

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Purpose of the study: To explore the consists of studying the opportunities for unlocking the potential of carrying out an entrepreneurial activity of big business within the Russian Arctic as a part of corporate social responsibility policy conducted by it. Methodology: The leading approach identified is the integrated approach which includes theoretical generalizations, analysis of practical activities of the corporations, and sociological methods of obtaining the information. Main Findings: The main concepts of big business management that contribute to boosting its entrepreneurial activity have been discussed, and opportunities for enhancing the entrepreneurial activity of big business within the Russian Arctic as a constituent of the corporate social responsibility policy have been found. Applications of this study: The materials of the paper are of practical importance for corporations working within the Russian Arctic. Novelty/Originality of this study: In the work, models of corporate social responsibility implemented by corporations of the Arctic states have been summarized.

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San Vicente Portes, Luis. "Aggregate Gains of International Diversification through Foreign Direct Investment: An Inquiry into the Moderation of U.S. Business Cycles." Global Economy Journal 7, no. 4 (October 2007): 1850120. http://dx.doi.org/10.2202/1524-5861.1284.

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Over the last 20 years the U.S. economy has experienced a strong reduction in the volatility of GDP growth. This paper documents and models the rapid growth of multinational corporations as a source of gradual decline in output and investment volatility. The paper introduces internationally diversified multinational firms into the financial accelerator framework; where international operations provide multinational firms with smoother paths of net worth that result in less volatile financing costs, investment and production. Model simulations suggest that larger multinational corporations can account for up to a 19 percent and 27 percent decline in output and investment volatility, respectively.

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Kovacova, Maria, and Tomas Kliestik. "Logit and Probit application for the prediction of bankruptcy in Slovak companies." Equilibrium 12, no. 4 (December 31, 2017): 775–91. http://dx.doi.org/10.24136/eq.v12i4.40.

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Research background: Prediction of bankruptcy is an issue of interest of various researchers and practitioners since the first study dedicated to this topic was published in 1932. Finding the suitable bankruptcy prediction model is the task for economists and analysts from all over the world. forecasting model using. Despite a large number of various models, which have been created by using different methods with the aim to achieve the best results, it is still challenging to predict bankruptcy risk, as corporations have become more global and more complex. Purpose of the article: The aim of the presented study is to construct, via an empirical study of relevant literature and application of suitable chosen mathematical statistical methods, models for bankruptcy prediction of Slovak companies and provide the comparison of overall prediction ability of the two developed models. Methods: The research was conducted on the data set of Slovak corporations covering the period of the year 2015, and two mathematical statistical methods were applied. The methods are logit and probit, which are both symmetric binary choice models, also known as conditional probability models. On the other hand, these methods show some significant differences in process of model formation, as well as in achieved results. Findings & Value added: Given the fact that mostly discriminant analysis and logistic regression are used for the construction of bankruptcy prediction models, we have focused our attention on the development bankruptcy prediction model in the Slovak Republic via logistic regression and probit. The results of the study suggest that the model based on a logit functions slightly outperforms the classification accuracy of probit model. Differences were obtained also in the detection of the most significant predictors of bankruptcy prediction in these types of models constructed in Slovak companies.

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Parisi, Giorgio, and Zhang Yi-Cheng. "Field theories and growth models." Journal of Statistical Physics 41, no. 1-2 (October 1985): 1–16. http://dx.doi.org/10.1007/bf01020601.

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Michor, Franziska. "Mathematical Models of Cancer Stem Cells." Journal of Clinical Oncology 26, no. 17 (June 10, 2008): 2854–61. http://dx.doi.org/10.1200/jco.2007.15.2421.

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Human cancers are thought to be sustained in their growth by a pathologic counterpart of normal adult stem cells: cancer stem cells. This concept was first developed in human myeloid leukemias and is today being extended to solid tumors such as breast and brain cancers. A quantitative understanding of cancer stem cells requires a mathematical framework to describe the dynamics of cancer initiation and progression, the response to treatment, and the evolution of resistance. In this review, I use chronic myeloid leukemia as an example to discuss how mathematical and computational techniques have been used to gain insights into the biology of cancer stem cells.

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Sheergojri, Aadil Rashid, Pervaiz Iqbal, and Ashiq Mohd Ilyas. "Cancer Growth Inhibition Using Predictive Mathematical Models of Signaling Pathways." International Journal of Statistics in Medical Research 10 (October 28, 2021): 132–35. http://dx.doi.org/10.6000/1929-6029.2021.10.12.

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Cancer cells develop several hallmark changes over the progress of the tumor process. Cell assistance in multicellular organisms is regulated by the division of cell coordination by aggressive growth modulation. In this perspective, the use of molecular indicators triggering cell division is a mystery, because a cancer cell can manipulate any molecule that induces and helps growth, disturbing cellular assistance. An effective alteration proceeding to tumors must develop to be competitive, allowing a cancer cell to pass a signal resulting in better selection chances. The subjective simulation of physiological systems has become increasingly valuable in recent years, and there is now a wide range of mathematical models of signalling pathways that have contributed to some groundbreaking discoveries and hypotheses as to how this system works. Here we discuss various modeling methods and their application to the physiology of medical systems, focusing on the identification of parameters in ordinary differential equation models and their significance for forecasting cellular decisions in network modeling. In situations of global and local cell-to-cell rivalry, we quantify how this mechanism impacts a mutated cell's fixing chance of producing such a signal, and consider that this process will play a vital role in reducing cancer.

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WALLS, ISABEL, VIRGINIA N. SCOTT, and DANE T. BERNARD. "Validation of Predictive Mathematical Models Describing Growth of Staphylococcus aureus." Journal of Food Protection 59, no. 1 (January 1, 1996): 11–15. http://dx.doi.org/10.4315/0362-028x-59.1.11.

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An investigation was performed on the growth of Staphylococcus aureus in a commercially available, sterile, homogeneous food at 12°C with 1.2 and 5.9% NaCl; at 25°C with 10.4% NaCl; and at 20 and 35°C with 1.2, 5.3, 12.5, and 15.8% NaCl; over a pH range of 5.5 to 7.5. Growth data were fitted to the Gompertz equation and the resulting growth kinetics were compared with predictions from the Pathogen Modeling Program (PMP) and Food MicroModel (FMM). For the PMP, predicted lag-phase durations varied from 0.5 to 130 h longer than the observed values. In general, close agreement with growth rates was obtained but there was a 10-fold difference in one case. For FMM, predicted lag-phase durations ranged from 27 h shorter to 47 h longer than the observed values. Again, close agreement with growth rates was obtained, but in one case a fivefold difference was observed. In general, for the sterile foods used under the growth conditions tested, the models underestimated the growth of S. aureus. This implies that while the models can be used as a guide to indicate growth rates in foods they should not be relied upon as the sole determinant of the product's safety.

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Anang, Asep, Iwan Setiawan, Heni Indrijani, and Limai Lan. "Mathematical Models of Growth and Feed Intake in Rambon Ducks." International Journal of Poultry Science 16, no. 11 (October 15, 2017): 457–61. http://dx.doi.org/10.3923/ijps.2017.457.461.

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BERGUER, R., J. L. BULL, and K. KHANAFER. "Refinements in Mathematical Models to Predict Aneurysm Growth and Rupture." Annals of the New York Academy of Sciences 1085, no. 1 (November 1, 2006): 110–16. http://dx.doi.org/10.1196/annals.1383.033.

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Nitzan, Meir, Ofer P. Tadmor, Yuri Skomorowski, Ron Rabinowitz, and Yoram Díamant. "Mathematical Models for Fetal Growth: Application for Biparietal Diameter Measurement." Fetal Diagnosis and Therapy 9, no. 5 (1994): 321–26. http://dx.doi.org/10.1159/000263955.

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Marušić, M., Ž. Bajzer, J. P. Freyer, and S. Vuk-Pavlović. "Analysis of growth of multicellular tumour spheroids by mathematical models." Cell Proliferation 27, no. 2 (February 1994): 73–94. http://dx.doi.org/10.1111/j.1365-2184.1994.tb01407.x.

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Heesterman, Berdine, John-Melle Bokhorst, Lisa de Pont, Berit Verbist, Jean-Pierre Bayley, Andel van der Mey, Eleonora Corssmit, Frederik Hes, Peter van Benthem, and Jeroen Jansen. "Mathematical Models for Tumor Growth and the Reduction of Overtreatment." Journal of Neurological Surgery Part B: Skull Base 80, no. 01 (July 23, 2018): 072–78. http://dx.doi.org/10.1055/s-0038-1667148.

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Background To improve our understanding of the natural course of head and neck paragangliomas (HNPGL) and ultimately differentiate between cases that benefit from early treatment and those that are best left untreated, we studied the growth dynamics of 77 HNPGL managed with primary observation. Methods Using digitally available magnetic resonance images, tumor volume was estimated at three time points. Subsequently, nonlinear least squares regression was used to fit seven mathematical models to the observed growth data. Goodness of fit was assessed with the coefficient of determination (R 2) and root-mean-squared error. The models were compared with Kruskal–Wallis one-way analysis of variance and subsequent post-hoc tests. In addition, the credibility of predictions (age at onset of neoplastic growth and estimated volume at age 90) was evaluated. Results Equations generating sigmoidal-shaped growth curves (Gompertz, logistic, Spratt and Bertalanffy) provided a good fit (median R 2: 0.996–1.00) and better described the observed data compared with the linear, exponential, and Mendelsohn equations (p < 0.001). Although there was no statistically significant difference between the sigmoidal-shaped growth curves regarding the goodness of fit, a realistic age at onset and estimated volume at age 90 were most often predicted by the Bertalanffy model. Conclusions Growth of HNPGL is best described by decelerating tumor growth laws, with a preference for the Bertalanffy model. To the best of our knowledge, this is the first time that this often-neglected model has been successfully fitted to clinically obtained growth data.

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