The poststagnation stage for mature tourism areas

2016 ◽  
Vol 23 (2) ◽  
pp. 387-402 ◽  
Author(s):  
Isabel P. Albaladejo ◽  
María Pilar Martínez-García
Keyword(s):  
Life Cycle ◽  
Growth Model ◽  
Logistic Model ◽  
Temporal Evolution ◽  
Growth Models ◽  
Life Cycles ◽  
Logistic Growth ◽  
Tourism Sector ◽  
Passenger Flows ◽  
Logistic Growth Model

The tourism area life cycle (TALC) model of Butler explains the temporal evolution of a tourism resort. Lundtorp and Wanhill find that the logistic growth model represents the first phases of the TALC model. However, since the logistic model assumes a fixed tourism market ceiling, it fails to explain the poststagnation stage, where rejuvenation, decline, or any other intermediate possibility may arise. Taking into account the data of passenger flows to Bornholm from 1912 to 2001 collected by Lundtorp and Wanhill, the authors find that the superposition of several logistic growth models fits better with these data. Then they propose a multilogistic growth model, where investment or innovation in the tourism sector boosts the addition of new logistic curves which superpose the old ones. The continuous birth and superposition of these new life cycles is not free; it requires the purposive effort of entrepreneurs and governments seeking new markets and the improvement of infrastructures.

scholarly journals Fitting a logistic growth model to yield traits of lettuce cultivars growing in summer

2020 ◽  
Vol 14 (1) ◽  
Author(s):  
Fernanda Carini ◽  
Alberto Cargnelutti-Filho ◽  
Jéssica Maronez De Souza ◽  
Rafael Vieira Pezzini ◽  
Cassiane Ubessi ◽  
...  
Keyword(s):  
Confidence Intervals ◽  
Growth Model ◽  
Iterative Process ◽  
Logistic Model ◽  
Goodness Of Fit ◽  
Least Squares Method ◽  
Logistic Growth ◽  
Model Parameters ◽  
Yield Traits ◽  
Logistic Growth Model

The objective of this study was to fit a logistic model to fresh and dry matters of leaves and fresh and dry matters of shoots of four lettuce cultivars to describe growth in summer. Cultivars Crocantela, Elisa, Rubinela, and Vera were evaluated in the summer of 2017 and 2018, in soil in protected environment and in soilless system. Seven days after transplantation, fresh and dry leaf matters and fresh and dry shoot matters of 8 plants were weighed every 4 days. The model parameters were estimated using the software R, using the least squares method and iterative process of Gauss-Newton. We also estimated the confidence intervals of the parameters, verified the assumptions of the models, calculated the goodness-of-fit measures and the critical points, and quantified the parametric and intrinsic nonlinearities. The logistic growth model fitted well to fresh and dry leaf and shoot matters of cultivars Crocantela, Elisa, Rubinela, and Vera and is indicated to describe the growth of lettuce.

scholarly journals Inventory models for short life cycle clothing products use a logistic growth model

2020 ◽  
Vol 1490 ◽  
pp. 012060
Author(s):  
V Lukitosari ◽  
A P Subriadi
Keyword(s):  
Life Cycle ◽  
Growth Model ◽  
Logistic Growth ◽  
Inventory Models ◽  
Short Life ◽  
Logistic Growth Model

Application of Logistic Model in Container Throughput Prediction

2013 ◽  
Vol 401-403 ◽  
pp. 2163-2165
Author(s):  
Pan Zheng ◽  
Shou Jiang Zhao ◽  
Hao Fan
Keyword(s):  
Growth Model ◽  
Logistic Model ◽  
Logistic Growth ◽  
Real Situation ◽  
The Real ◽  
Limited Space ◽  
Logistic Growth Model

Logistic growth model is important for studying the increasing laws in limited space. In this paper, the container throughput of Wuhan xingang has been studied by using logistic model. Compared with the actual throughput, container throughput prediction using logistic growth model is in accordance with the real situation on the whole.

scholarly journals Malthusian Catastrophe: Species Extinction Caused by Oversized Population

2017 ◽  
Author(s):  
Xubin Pan
Keyword(s):  
Lower Limit ◽  
Growth Model ◽  
Logistic Model ◽  
Allee Effect ◽  
Extinction Risk ◽  
Logistic Growth ◽  
Extinction Debt ◽  
Species Extinction ◽  
Upper Limit ◽  
Logistic Growth Model

AbstractThere is one pseudo-extinction debt and four occurring conditions for real extinction debt. Since small and oversized populations have a high extinction risk, Pan threshold (upper limit) was calculated for Verhulst-Pear “logistic” growth model and logistic model with the Allee effect, an important parameter corresponding to Allee threshold (lower limit).

scholarly journals Logistic growth models of China pinks, cultivated on seven substrates, as a function of degree days

2016 ◽  
Vol 46 (11) ◽  
pp. 1924-1931
Author(s):  
Marília Milani ◽  
Sidinei José Lopes ◽  
Rogério Antônio Bellé ◽  
Fernanda Alice Antonello Londero Backes
Keyword(s):  
Growth Model ◽  
Rice Husk ◽  
Rice Husk Ash ◽  
Growth Models ◽  
Growth Period ◽  
Leaf Number ◽  
Logistic Growth ◽  
Maximum Height ◽  
Degree Days ◽  
Logistic Growth Model

ABSTRACT: The objective of this study was to characterize the height (H) and leaf number (LN) of China pinks, grown in seven substrates, as a function of degree days, using the logistic growth model. H and LN were measured from 56 plants per substrate, for 392 plants in total. Plants that were grown on substrates formed of 50% soil with 50% rice husk ash (50% S + 50% RH) and 80% rice husk ash with 20% worm castings (80% RH + 20% W) had the longest vegetative growth period (74d), corresponding to 1317.9ºCd. The logistic growth model, adjusted for H, showed differences in the estimation of maximum expected height (α) between the substrates, with values between 10.47cm for 50% S + 50% RH and 35.75cm for Mecplant(r). When α was estimated as LN, variation was also observed between the different substrates, from approximately 30 leaves on plants growing on 50% S + 50% RH to 34 leaves on the plants growing on the substrate formed of 80% RH + 20% W. Growth of China pinks can be characterized using H or LN in the logistic growth model as a function of degree days, being the provided plants adequately fertilized. The best substrates in terms of maximum height and leaf number were 80% soil + 20% worm castings and Mecplant(r). However, users must recalibrate the model with the estimated parameters before applying it to different growing conditions.

A Logistic Growth Model for Software Reliability Estimation Considering Uncertain Factors

2021 ◽  
pp. 2150032
Author(s):  
Md. Asraful Haque ◽  
Nesar Ahmad
Keyword(s):  
Growth Model ◽  
Software Reliability ◽  
Growth Models ◽  
Reliability Estimation ◽  
Logistic Growth ◽  
Systematic Analysis ◽  
Software Reliability Growth ◽  
Logistic Growth Model ◽  
Operational Phase ◽  
Special Parameter

Software reliability growth models (SRGMs) are widely used to estimate software reliability by analyzing failure dataset throughout the testing process. A large number of SRGMs have been proposed on a regular basis by researchers since the 1970s. They are represented with a set of assumptions and a set of parameters. One major problem in SRGMs is that the uncertainties surrounding the assumptions and parameters are generally not taken into account by most of them. Therefore, sometimes, the predicted reliability on testing phase significantly varies in actual operational phase. This paper presents a logistic growth model that incorporates a special parameter to consider the effects of all possible uncertainties. A systematic analysis is carried out to identify the major uncertain factors and their impacts on the fault detection rate. The applicability of the model is shown by validating it on two different real datasets that are commonly used in various studies. The comparisons with nine established models in terms of mean square error (MSE), variance, predictive-ratio risk (PRR), [Formula: see text]and AIC have been presented.

Optimal Harvesting in the Stochastic Logistic Growth Model with Finite Horizon

2013 ◽  
Vol 51 (3) ◽  
pp. 2335-2355 ◽  
Author(s):  
Hiroaki Morimoto
Keyword(s):  
Growth Model ◽  
Finite Horizon ◽  
Logistic Growth ◽  
Optimal Harvesting ◽  
Logistic Growth Model

scholarly journals Extended logistic growth model for heterogeneous populations

2017 ◽  
Author(s):  
Wang Jin ◽  
Scott W McCue ◽  
Matthew J Simpson
Keyword(s):  
Cell Proliferation ◽  
Growth Model ◽  
Numerical Solutions ◽  
Cellular Level ◽  
Logistic Growth ◽  
Logistic Growth Model ◽  
Living Tissues ◽  
Discrete Mathematical Model ◽  
The Continuum

AbstractCell proliferation is the most important cellular-level mechanism responsible for regulating cell population dynamics in living tissues. Modern experimental procedures show that the proliferation rates of individual cells can vary significantly within the same cell line. However, in the mathematical biology literature, cell proliferation is typically modelled using a classical logistic equation which neglects variations in the proliferation rate. In this work, we consider a discrete mathematical model of cell migration and cell proliferation, modulated by volume exclusion (crowding) effects, with variable rates of proliferation across the total population. We refer to this variability as heterogeneity. Constructing the continuum limit of the discrete model leads to a generalisation of the classical logistic growth model. Comparing numerical solutions of the model to averaged data from discrete simulations shows that the new model captures the key features of the discrete process. Applying the extended logistic model to simulate a proliferation assay using rates from recent experimental literature shows that neglecting the role of heterogeneity can, at times, lead to misleading results.

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