A New Flexible Sigmoidal Growth Model

Biological growth is driven by numerous functions, such as hormones and mineral nutrients, and is also involved in various ecological processes. Therefore, it is necessary to accurately capture the growth trajectory of various species in ecosystems. A new sigmoidal growth (NSG) model is presented he...
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Autor*in:	Liying Cao [verfasserIn] Pei-Jian Shi [verfasserIn] Lin Li [verfasserIn] Guifen Chen [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2019

Schlagwörter:	growth model asymmetric growth rate curve biological growth new sigmoidal growth (NSG)

Übergeordnetes Werk:	In: Symmetry - MDPI AG, 2009, 11(2019), 2, p 204
Übergeordnetes Werk:	volume:11 ; year:2019 ; number:2, p 204

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/sym11020204

Katalog-ID:	DOAJ032872534

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spelling	10.3390/sym11020204 doi (DE-627)DOAJ032872534 (DE-599)DOAJ5581bb5cd53f49dea3dee5ca519086ba DE-627 ger DE-627 rakwb eng QA1-939 Liying Cao verfasserin aut A New Flexible Sigmoidal Growth Model 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Biological growth is driven by numerous functions, such as hormones and mineral nutrients, and is also involved in various ecological processes. Therefore, it is necessary to accurately capture the growth trajectory of various species in ecosystems. A new sigmoidal growth (NSG) model is presented here for describing the growth of animals and plants when the assumption is that the growth rate curve is asymmetric. The NSG model was compared with four classic sigmoidal growth models, including the logistic equation, Richards, Gompertz, and ontogenetic growth models. Results indicated that all models fit well with the empirical growth data of 12 species, except the ontogenetic growth model, which only captures the growth of animals. The estimated maximum asymptotic biomass <inline-formula< <math display="inline"< <semantics< <mrow< <msub< <mi<w</mi< <mrow< <mi<m</mi< <mi<a</mi< <mi<x</mi< </mrow< </msub< </mrow< </semantics< </math< </inline-formula< of plants from the ontogenetic growth model was not reliable. The experiment result shows that the NSG model can more precisely estimate the value and time of reaching maximum biomass when growth rate becomes close to zero near the end of growth. The NSG model contains three other parameters besides the value and time of reaching maximum biomass, and thereby, it can be difficult to assign initial values for parameterization using local optimization methods (e.g., using Gauss⁻Newton or Levenberg⁻Marquardt methods). We demonstrate the use of a differential evolution algorithm for resolving this issue efficiently. As such, the NSG model can be applied to describing the growth patterns of a variety of species and estimating the value and time of achieving maximum biomass simultaneously. growth model asymmetric growth rate curve biological growth new sigmoidal growth (NSG) Mathematics Pei-Jian Shi verfasserin aut Lin Li verfasserin aut Guifen Chen verfasserin aut In Symmetry MDPI AG, 2009 11(2019), 2, p 204 (DE-627)610604112 (DE-600)2518382-5 20738994 nnns volume:11 year:2019 number:2, p 204 https://doi.org/10.3390/sym11020204 kostenfrei https://doaj.org/article/5581bb5cd53f49dea3dee5ca519086ba kostenfrei https://www.mdpi.com/2073-8994/11/2/204 kostenfrei https://doaj.org/toc/2073-8994 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2019 2, p 204
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callnumber-first	Q - Science
author	Liying Cao
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topic_title	QA1-939 A New Flexible Sigmoidal Growth Model growth model asymmetric growth rate curve biological growth new sigmoidal growth (NSG)
topic	misc QA1-939 misc growth model misc asymmetric growth rate curve misc biological growth misc new sigmoidal growth (NSG) misc Mathematics
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title	A New Flexible Sigmoidal Growth Model
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title_full	A New Flexible Sigmoidal Growth Model
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title_auth	A New Flexible Sigmoidal Growth Model
abstract	Biological growth is driven by numerous functions, such as hormones and mineral nutrients, and is also involved in various ecological processes. Therefore, it is necessary to accurately capture the growth trajectory of various species in ecosystems. A new sigmoidal growth (NSG) model is presented here for describing the growth of animals and plants when the assumption is that the growth rate curve is asymmetric. The NSG model was compared with four classic sigmoidal growth models, including the logistic equation, Richards, Gompertz, and ontogenetic growth models. Results indicated that all models fit well with the empirical growth data of 12 species, except the ontogenetic growth model, which only captures the growth of animals. The estimated maximum asymptotic biomass <inline-formula< <math display="inline"< <semantics< <mrow< <msub< <mi<w</mi< <mrow< <mi<m</mi< <mi<a</mi< <mi<x</mi< </mrow< </msub< </mrow< </semantics< </math< </inline-formula< of plants from the ontogenetic growth model was not reliable. The experiment result shows that the NSG model can more precisely estimate the value and time of reaching maximum biomass when growth rate becomes close to zero near the end of growth. The NSG model contains three other parameters besides the value and time of reaching maximum biomass, and thereby, it can be difficult to assign initial values for parameterization using local optimization methods (e.g., using Gauss⁻Newton or Levenberg⁻Marquardt methods). We demonstrate the use of a differential evolution algorithm for resolving this issue efficiently. As such, the NSG model can be applied to describing the growth patterns of a variety of species and estimating the value and time of achieving maximum biomass simultaneously.
abstractGer	Biological growth is driven by numerous functions, such as hormones and mineral nutrients, and is also involved in various ecological processes. Therefore, it is necessary to accurately capture the growth trajectory of various species in ecosystems. A new sigmoidal growth (NSG) model is presented here for describing the growth of animals and plants when the assumption is that the growth rate curve is asymmetric. The NSG model was compared with four classic sigmoidal growth models, including the logistic equation, Richards, Gompertz, and ontogenetic growth models. Results indicated that all models fit well with the empirical growth data of 12 species, except the ontogenetic growth model, which only captures the growth of animals. The estimated maximum asymptotic biomass <inline-formula< <math display="inline"< <semantics< <mrow< <msub< <mi<w</mi< <mrow< <mi<m</mi< <mi<a</mi< <mi<x</mi< </mrow< </msub< </mrow< </semantics< </math< </inline-formula< of plants from the ontogenetic growth model was not reliable. The experiment result shows that the NSG model can more precisely estimate the value and time of reaching maximum biomass when growth rate becomes close to zero near the end of growth. The NSG model contains three other parameters besides the value and time of reaching maximum biomass, and thereby, it can be difficult to assign initial values for parameterization using local optimization methods (e.g., using Gauss⁻Newton or Levenberg⁻Marquardt methods). We demonstrate the use of a differential evolution algorithm for resolving this issue efficiently. As such, the NSG model can be applied to describing the growth patterns of a variety of species and estimating the value and time of achieving maximum biomass simultaneously.
abstract_unstemmed	Biological growth is driven by numerous functions, such as hormones and mineral nutrients, and is also involved in various ecological processes. Therefore, it is necessary to accurately capture the growth trajectory of various species in ecosystems. A new sigmoidal growth (NSG) model is presented here for describing the growth of animals and plants when the assumption is that the growth rate curve is asymmetric. The NSG model was compared with four classic sigmoidal growth models, including the logistic equation, Richards, Gompertz, and ontogenetic growth models. Results indicated that all models fit well with the empirical growth data of 12 species, except the ontogenetic growth model, which only captures the growth of animals. The estimated maximum asymptotic biomass <inline-formula< <math display="inline"< <semantics< <mrow< <msub< <mi<w</mi< <mrow< <mi<m</mi< <mi<a</mi< <mi<x</mi< </mrow< </msub< </mrow< </semantics< </math< </inline-formula< of plants from the ontogenetic growth model was not reliable. The experiment result shows that the NSG model can more precisely estimate the value and time of reaching maximum biomass when growth rate becomes close to zero near the end of growth. The NSG model contains three other parameters besides the value and time of reaching maximum biomass, and thereby, it can be difficult to assign initial values for parameterization using local optimization methods (e.g., using Gauss⁻Newton or Levenberg⁻Marquardt methods). We demonstrate the use of a differential evolution algorithm for resolving this issue efficiently. As such, the NSG model can be applied to describing the growth patterns of a variety of species and estimating the value and time of achieving maximum biomass simultaneously.
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title_short	A New Flexible Sigmoidal Growth Model
url	https://doi.org/10.3390/sym11020204 https://doaj.org/article/5581bb5cd53f49dea3dee5ca519086ba https://www.mdpi.com/2073-8994/11/2/204 https://doaj.org/toc/2073-8994
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The experiment result shows that the NSG model can more precisely estimate the value and time of reaching maximum biomass when growth rate becomes close to zero near the end of growth. The NSG model contains three other parameters besides the value and time of reaching maximum biomass, and thereby, it can be difficult to assign initial values for parameterization using local optimization methods (e.g., using Gauss⁻Newton or Levenberg⁻Marquardt methods). We demonstrate the use of a differential evolution algorithm for resolving this issue efficiently. 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