Journal articles: 'Logistic growth'

1

Meyer, Perrin. "Bi-logistic growth." Technological Forecasting and Social Change 47, no. 1 (September 1994): 89–102. http://dx.doi.org/10.1016/0040-1625(94)90042-6.

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Hieber, Matthias, Pablo Koch Medina, and Sandro Merino. "Diffusive logistic growth on." Nonlinear Analysis: Theory, Methods & Applications 27, no. 8 (October 1996): 879–94. http://dx.doi.org/10.1016/0362-546x(95)00035-t.

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3

Tsoularis, A., and J. Wallace. "Analysis of logistic growth models." Mathematical Biosciences 179, no. 1 (July 2002): 21–55. http://dx.doi.org/10.1016/s0025-5564(02)00096-2.

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4

Kucharavy, Dmitry, and Roland De Guio. "Application of Logistic Growth Curve." Procedia Engineering 131 (2015): 280–90. http://dx.doi.org/10.1016/j.proeng.2015.12.390.

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5

Gopalsamy, K., and Pei-Xuan Weng. "Feedback regulation of logistic growth." International Journal of Mathematics and Mathematical Sciences 16, no. 1 (1993): 177–92. http://dx.doi.org/10.1155/s0161171293000213.

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Sufficient conditions are obtained for the global asymptotic stability of the positive equilibrium of a regulated logistic growth with a delay in the state feedback of the control modelled bydn(t)dt=rn(t)[1−(a1n(t)+a2n(t−τ)K)−cu(t)]dn(t)dt=−au(t)+bn(t−τ)whereudenotes an indirect control variable,r,a2,τ,a,b,c∈(0,∞)anda1∈[0,∞).

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6

Shah, Md Asaduzzaman. "Stochastic Logistic Model for Fish Growth." Open Journal of Statistics 04, no. 01 (2014): 11–18. http://dx.doi.org/10.4236/ojs.2014.41002.

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Awadalla, Muath, Yves Yannick Yameni Noupoue, and Kinda Abu Asbeh. "Psi-Caputo Logistic Population Growth Model." Journal of Mathematics 2021 (July 26, 2021): 1–9. http://dx.doi.org/10.1155/2021/8634280.

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This article studies modeling of a population growth by logistic equation when the population carrying capacity K tends to infinity. Results are obtained using fractional calculus theories. A fractional derivative known as psi-Caputo plays a substantial role in the study. We proved existence and uniqueness of the solution to the problem using the psi-Caputo fractional derivative. The Chinese population, whose carrying capacity, K, tends to infinity, is used as evidence to prove that the proposed approach is appropriate and performs better than the usual logistic growth equation for a population with a large carrying capacity. A psi-Caputo logistic model with the kernel function x + 1 performed the best as it minimized the error rate to 3.20% with a fractional order of derivative α = 1.6455.

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Antonelli, Peter L. "Filtering then-dimensional logistic growth model." Stochastic Analysis and Applications 8, no. 3 (January 1990): 263–92. http://dx.doi.org/10.1080/07362999008809209.

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9

Webb, G. F. "Logistic models of structured population growth." Computers & Mathematics with Applications 12, no. 4-5 (April 1986): 527–39. http://dx.doi.org/10.1016/0898-1221(86)90178-1.

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10

TUCKWELL, H., and J. KOZIOL. "Logistic population growth under random dispersal." Bulletin of Mathematical Biology 49, no. 4 (1987): 495–506. http://dx.doi.org/10.1016/s0092-8240(87)80010-1.

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11

Lambert, Amaury. "The branching process with logistic growth." Annals of Applied Probability 15, no. 2 (May 2005): 1506–35. http://dx.doi.org/10.1214/105051605000000098.

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12

Harris, S. "Diffusive logistic population growth with immigration." Applied Mathematics Letters 18, no. 3 (March 2005): 261–65. http://dx.doi.org/10.1016/j.aml.2003.03.009.

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13

Tuckwell, Henry C., and James A. Koziol. "Logistic population growth under random dispersal." Bulletin of Mathematical Biology 49, no. 4 (July 1987): 495–506. http://dx.doi.org/10.1007/bf02458866.

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14

Khadim, Zunaira, Irem Batool, Ahsan Akbar, Petra Poulova, and Minahs Akbar. "Mapping the Moderating Role of Logistics Performance of Logistics Infrastructure on Economic Growth in Developing Countries." Economies 9, no. 4 (November 11, 2021): 177. http://dx.doi.org/10.3390/economies9040177.

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Logistics performance is an important determinant of economic growth. The present study investigates the moderating role of logistics performance of the logistic infrastructure on economic growth in developing countries. We employ the World Bank computed LPI index in the year 2010, 2012, 2014, 2016 and 2018 to measure the logistic performance. The current research includes the 50 developing economies, and a panel data set comprising of total 300 observations is collected. The study used the conventional Cobb–Douglas production function with labor, capital stock as main drivers of economic growth. The study found that the labor and capital endowments have significantly different impacts in terms of elasticity coefficients for developing countries with different logistics performance levels. It implies that logistics performance, i.e., the efficient performance of logistic infrastructure, plays a moderator role in economic growth in developing economies.

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15

Khodabin, Morteza, and Neda Kiaee. "Stochastic Dynamical Theta-Logistic Population Growth Model." SOP Transactions on Statistics and Analysis 2014, no. 3 (October 31, 2014): 1–15. http://dx.doi.org/10.15764/stsa.2014.03001.

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FUJIKAWA, Hiroshi, Akemi KAI, and Satoshi MOROZUMI. "A New Logistic Model for Bacterial Growth." Journal of the Food Hygienic Society of Japan (Shokuhin Eiseigaku Zasshi) 44, no. 3 (2003): 155–60. http://dx.doi.org/10.3358/shokueishi.44.155.

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17

Xiao, Lan, and Yanqiu Chen. "Improvement and Application of Logistic Growth Model." Scholars Journal of Physics, Mathematics and Statistics 7, no. 9 (September 19, 2020): 192–96. http://dx.doi.org/10.36347/sjpms.2020.v07i09.002.

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18

Quezada-Téllez, L. A., and L. Franco-Pérez. "A fractional logistic approach for economic growth." International Journal of Modern Physics C 29, no. 12 (December 2018): 1850123. http://dx.doi.org/10.1142/s0129183118501231.

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A discrete logistic model is proposed for the Gross Domestic Product of an economy and its parameters are adjusted for some specific countries, taken from the Organization for Economic Co-operation and Development databases. Although this model fits “adequately” for some countries analyzed in this work, others show that it could be enhanced. A Caputo like fractional discrete model as a fractional version of the logistic one is presented, with the parameters stated previously. It is shown, by using the mean squared error, that this fractional version fits better for an economy whose behavior shows over time an isolated and unexpected boom period in its economy growth followed by a crisis period. For this type of dynamics, the fractional-order logistic equation improves the modeling given by an integer order logistic equation.

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19

Padrón, Víctor, and María Cristina Trevisan. "Environmentally induced dispersal under heterogeneous logistic growth." Mathematical Biosciences 199, no. 2 (February 2006): 160–74. http://dx.doi.org/10.1016/j.mbs.2005.11.004.

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20

Jin, Wang, Scott W. McCue, and Matthew J. Simpson. "Extended logistic growth model for heterogeneous populations." Journal of Theoretical Biology 445 (May 2018): 51–61. http://dx.doi.org/10.1016/j.jtbi.2018.02.027.

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21

Gatto, M., S. Muratori, and S. Rinaldi. "On the optimality of the logistic growth." Journal of Optimization Theory and Applications 57, no. 3 (June 1988): 513–17. http://dx.doi.org/10.1007/bf02346168.

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22

Thornley, John H. M., and James France. "An open-ended logistic-based growth function." Ecological Modelling 184, no. 2-4 (June 2005): 257–61. http://dx.doi.org/10.1016/j.ecolmodel.2004.10.007.

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23

Invernizzi, Sergio, and Katia Terpin. "A generalized logistic model for photosynthetic growth." Ecological Modelling 94, no. 2-3 (January 1997): 231–42. http://dx.doi.org/10.1016/s0304-3800(96)00024-5.

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24

Choi, Jaehwa, Jeffrey R. Harring, and Gregory R. Hancock. "Latent Growth Modeling for Logistic Response Functions." Multivariate Behavioral Research 44, no. 5 (September 30, 2009): 620–45. http://dx.doi.org/10.1080/00273170903187657.

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25

Pham, Hoang. "A Generalized Logistic Software Reliability Growth Model." OPSEARCH 42, no. 4 (December 2005): 322–31. http://dx.doi.org/10.1007/bf03398744.

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26

Nong, Yu, and Qingyun Du. "Urban growth pattern modeling using logistic regression." Geo-spatial Information Science 14, no. 1 (January 2011): 62–67. http://dx.doi.org/10.1007/s11806-011-0427-x.

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27

Meyer, Perrin S., Jason W. Yung, and Jesse H. Ausubel. "A Primer on Logistic Growth and Substitution." Technological Forecasting and Social Change 61, no. 3 (July 1999): 247–71. http://dx.doi.org/10.1016/s0040-1625(99)00021-9.

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28

Namkoong, Gene, and James F. Selgrade. "Frequency-dependent selection in logistic growth models." Theoretical Population Biology 29, no. 1 (February 1986): 64–86. http://dx.doi.org/10.1016/0040-5809(86)90005-5.

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29

Kemmerly, Phillip R. "Modeling doline populations with logistic growth functions." Earth Surface Processes and Landforms 32, no. 4 (2007): 587–601. http://dx.doi.org/10.1002/esp.1420.

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30

FARIA, ROSANE NUNES DE, CAIO SILVESTRE DE SOUZA, and JOSÉ GERALDO VIDAL VIEIRA. "EVALUATION OF LOGISTIC PERFORMANCE INDEXES OF BRAZIL IN THE INTERNATIONAL TRADE." RAM. Revista de Administração Mackenzie 16, no. 1 (February 2015): 213–35. http://dx.doi.org/10.1590/1678-69712015/administracao.v16n1p213-235.

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The importance of efficient logistics for trade growth is widely acknowledged. Literature has shown that better logistics performance is strongly associated with trade expansion, export diversification, ability to attract foreign direct investments, and economic growth. On the other hand, international trade represents a challenge to logistic operations in transporting and storing products. High logistic costs and low quality of services may be considered obstacles to international trade. This research aims to assess Brazil’s Logistics Performance Index (LPI) in relation to its major competitors in international trade. The international trade data was collected from SECEX and COMTRADE, while the LPI was provided by the World Bank. Statistical techniques such as cluster analysis and multiple comparison tests of means have been applied to analyze the data. After using LPI index for the 39 competitors, it has been observed that Brazil occupies the 26th position in the rank of performers, behind South Africa, Kuwait and Saudi Arabia. The top performers are in general the leading exporters and importers worldwide (Germany, U.S.A., Japan and the Netherlands). Furthermore, they are the strongest competitors of Brazil in international trade. Thus, the competitiveness of Brazilian domestic firms depends crucially on a dynamic and competitive internal logistic environment in order to stand up to these countries. The results also indicate the bureaucracy as a major obstacle to the logistic performance of the country. The dimension Timeliness of Brazil is very close to the High Logistics Performance Group (HLPG) while Customs is very close to the Low Logistics Performance Group (LLPG). Although Brazil has failed in its customs operations, there seems to be more credibility in Brazilian dealings. The main contribution of this paper is to reveal logistical aspects in which Brazil has shown large inefficiencies. The difference among the logistic performance indexs also appears to be relevant to governments to address their new public policies and also to highlight the logistic obstacles of the Brazilian international trade.

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31

Khalili Golmankhaneh, Alireza, and Carlo Cattani. "Fractal Logistic Equation." Fractal and Fractional 3, no. 3 (July 11, 2019): 41. http://dx.doi.org/10.3390/fractalfract3030041.

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In this paper, we give difference equations on fractal sets and their corresponding fractal differential equations. An analogue of the classical Euler method in fractal calculus is defined. This fractal Euler method presets a numerical method for solving fractal differential equations and finding approximate analytical solutions. Fractal differential equations are solved by using the fractal Euler method. Furthermore, fractal logistic equations and functions are given, which are useful in modeling growth of elements in sciences including biology and economics.

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32

FUJIKAWA, Hiroshi, Akemi KAI, and Satoshi MOROZUMI. "Improvement of New Logistic Model for Bacterial Growth." Journal of the Food Hygienic Society of Japan (Shokuhin Eiseigaku Zasshi) 45, no. 5 (2004): 250–54. http://dx.doi.org/10.3358/shokueishi.45.250.

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33

Trofimchuk, E., and Sergei Trofimchuk. "Global stability in a regulated logistic growth model." Discrete and Continuous Dynamical Systems - Series B 5, no. 2 (February 2005): 461–68. http://dx.doi.org/10.3934/dcdsb.2005.5.461.

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34

Ben Alì Zinati, Riccardo, Charlie Duclut, Saeed Mahdisoltani, Andrea Gambassi, and Ramin Golestanian. "Stochastic dynamics of chemotactic colonies with logistic growth." Europhysics Letters 136, no. 5 (December 1, 2021): 50003. http://dx.doi.org/10.1209/0295-5075/ac48c9.

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Abstract The interplay between cellular growth and cell-cell signaling is essential for the aggregation and proliferation of bacterial colonies, as well as for the self-organization of cell tissues. To investigate this interplay, we focus here on the collective properties of dividing chemotactic cell colonies by studying their long-time and large-scale dynamics through a renormalization group (RG) approach. The RG analysis reveals that a relevant but unconventional chemotactic interaction —corresponding to a polarity-induced mechanism— is generated by fluctuations at macroscopic scales, even when an underlying mechanism is absent at the microscopic level. This emerges from the interplay of the well-known Keller-Segel (KS) chemotactic nonlinearity and cell birth and death processes. At one-loop order, we find no stable fixed point of the RG flow equations. We discuss a connection between the dynamics investigated here and the celebrated Kardar-Parisi-Zhang (KPZ) equation with long-range correlated noise, which points at the existence of a strong-coupling, nonperturbative fixed point.

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35

Gao, Daozhou, and Shigui Ruan. "A Multipatch Malaria Model with Logistic Growth Populations." SIAM Journal on Applied Mathematics 72, no. 3 (January 2012): 819–41. http://dx.doi.org/10.1137/110850761.

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36

Grossman, M., and B. B. Bohren. "Logistic growth curve of chickens: heritability of parameters." Journal of Heredity 76, no. 6 (November 1985): 459–62. http://dx.doi.org/10.1093/oxfordjournals.jhered.a110145.

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37

FORYŚ, URSZULA, JAN POLESZCZUK, and TING LIU. "Logistic Tumor Growth with Delay and Impulsive Treatment." Mathematical Population Studies 21, no. 3 (July 3, 2014): 146–58. http://dx.doi.org/10.1080/08898480.2013.804688.

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38

Xuemei, Hu. "The indirect method for stochastic logistic growth models." Communications in Statistics - Theory and Methods 46, no. 3 (March 8, 2016): 1506–18. http://dx.doi.org/10.1080/03610926.2015.1019152.

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39

Chang, Der-Chen, and Duy-Minh Nhieu ‡. "A Difference Equation Arising from Logistic Population Growth." Applicable Analysis 83, no. 6 (June 2004): 579–98. http://dx.doi.org/10.1080/00036810410001649692.

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40

Kink, Peter. "Some analysis of a stochastic logistic growth model." Stochastic Analysis and Applications 36, no. 2 (December 7, 2017): 240–56. http://dx.doi.org/10.1080/07362994.2017.1393343.

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41

Bogdan, Marcel. "Algebraic Time-Rate Condition for Logistic Growth Models." Procedia Manufacturing 46 (2020): 539–42. http://dx.doi.org/10.1016/j.promfg.2020.03.077.

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42

Song, Yongli, and Sanling Yuan. "Bifurcation analysis for a regulated logistic growth model." Applied Mathematical Modelling 31, no. 9 (September 2007): 1729–38. http://dx.doi.org/10.1016/j.apm.2006.06.006.

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43

Sancho, Pedro. "Error growth in the time-dependent logistic equation." Chaos, Solitons & Fractals 35, no. 1 (January 2008): 133–39. http://dx.doi.org/10.1016/j.chaos.2006.05.033.

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44

Hartz, S. M., Y. Ben-Shahar, and M. Tyler. "Logistic growth curve analysis in associative learning data." Animal Cognition 3, no. 4 (March 1, 2001): 185–89. http://dx.doi.org/10.1007/s100710000075.

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45

Modis, Theodore. "US Nobel laureates: Logistic growth versus Volterra–Lotka." Technological Forecasting and Social Change 78, no. 4 (May 2011): 559–64. http://dx.doi.org/10.1016/j.techfore.2010.10.002.

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46

Hu, Zhiyong, and C. P. Lo. "Modeling urban growth in Atlanta using logistic regression." Computers, Environment and Urban Systems 31, no. 6 (November 2007): 667–88. http://dx.doi.org/10.1016/j.compenvurbsys.2006.11.001.

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47

Abdul Latif, Nurul Syaza, Noor Asma’ Mohd Anuar Mushoddad, and Norin Syerina Mior Azmai. "Agriculture Management Strategies Using Simple Logistic Growth Model." IOP Conference Series: Earth and Environmental Science 596 (December 29, 2020): 012076. http://dx.doi.org/10.1088/1755-1315/596/1/012076.

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48

Mulligan, Gordon F. "Logistic Population Growth in the World's Largest Cities." Geographical Analysis 38, no. 4 (October 2006): 344–70. http://dx.doi.org/10.1111/j.1538-4632.2006.00690.x.

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49

Abdullah Al Jabri, Asma Mohammed, Zouhaier Slimi, Hind Huwaishal Al Yaqoopi, and Umut Mehmet. "Logistic Companies in Oman: Role in Boosting Economy, Implementing Eco-Friendly, Technological Logistics for Sustainable Development." European Journal of Business and Management Research 6, no. 5 (October 22, 2021): 209–18. http://dx.doi.org/10.24018/ejbmr.2021.6.5.1127.

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This article focuses on the role of logistic companies in economic growth and how eco-friendly are implementing eco-friendly technology, green logistics, and considering the United Nations Sustainable Development Goals. This research aims to study the logistic sector in Oman and how its practices could be changed into eco-friendly processes to conserve the environment. The study highlights consumption, which encourages the adoption of green practices. Moreover, the research pays attention to the drivers and barriers to green adoption initiatives. The research used quantitative methods in the form of surveys with the help of qualitative evidence from the literature to reach outcomes. The findings vindicate that Oman places great emphasis on the development of logistics services to achieve economic growth. The implementation of green technology allows companies to help the environment and cost savings. Studies are helpful if managers in the business are willing to bring such practices into their business. There is a need to align sustainable development and logistics operations to support the SDGs in their CSR initiative. The shift usually takes a long time, but the initiative must be taken, and governmental bodies must support the greening of the logistic industry. Research findings reasonably advocate the share of the logistic industry in economic growth, so investments in this sector would only lead to better trade and more earnings whilst conforming to the customers' demands. The study faces some limitations, including a limitation and superficiality in the information presented in the green logistics aspect, the COVID-19 outbreak made it challenging to reach the target people of the survey.

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50

Thornley, John H. M., John J. Shepherd, and J. France. "An open-ended logistic-based growth function: Analytical solutions and the power-law logistic model." Ecological Modelling 204, no. 3-4 (June 2007): 531–34. http://dx.doi.org/10.1016/j.ecolmodel.2006.12.026.

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Journal articles on the topic 'Logistic growth'

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