Causes of Two Sets of Population Growth Data and Data Adjustment

We implement the Bayesian statistical inversion theory to obtain the solution for an inverse problem of growth data, using a fractional population growth model. We estimate the parameters in the model and we make a comparison between this model and an exponential one, based on an approximation of Bayes factor. A simulation study is carried out to show the performance of the estimators and the Bayes factor. Finally, we present a real data example to illustrate the effectiveness of the method proposed here and the pertinence of using a fractional model.

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Assessing Degrees of Anthropization on the Coast of Mexico from Ecosystem Conservation and Population Growth Data

Journal of Coastal Research ◽

10.2112/si92-016.1 ◽

2019 ◽

Vol 92 (sp1) ◽

pp. 136 ◽

Cited By ~ 2

Author(s):

Cesia J. Cruz ◽

Edgar Mendoza ◽

Rodolfo Silva ◽

Valeria Chávez

Keyword(s):

Population Growth ◽

Growth Data ◽

Ecosystem Conservation

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The Special Case of Retirees

Movers and Stayers ◽

10.1093/oso/9780190052898.003.0007 ◽

2021 ◽

pp. 161-174

Author(s):

Irwin L. Morris

Keyword(s):

Population Growth ◽

Retirement Age ◽

Growth Data ◽

Age In Place ◽

Distinctive Group ◽

Special Case

Retirees are a distinctive group within the set of southern movers. Significantly, they tend to be older and more conservative than other movers. Mobile retirees tend to congregate in certain areas, and so they may dampen the progressive effects normally associated with population growth. Data presented in Chapter 7 demonstrates that movers of retirement age are distinctive; they are more likely identify as Democrats, and they are more liberal than retirees who age in place. Given the partisan and political leanings of mobile retirees, it is unlikely that they have a significant dampening effect on the liberalizing impact of population growth more generally.

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Modelagem otimizada de indicadores biológicos de qualidade da água

Revista DAE ◽

10.36659/dae.2020.011 ◽

2019 ◽

Vol 221 (68) ◽

pp. 131-141

Author(s):

Gabriel da Costa Cantos Jerônimo ◽

Luiz Felipe Ramos Turci ◽

Paulo Augusto Zaitune Pamplin ◽

Patrícia Neves Mendes

Keyword(s):

Population Growth ◽

Logistic Model ◽

Growth Models ◽

Lemna Minor ◽

Growth Data ◽

Model Quality ◽

Nonlinear Growth ◽

Statistical Indicators ◽

Selection Of

Resumo 27/06/2018 DOI: https://doi.org/10.36659 /dae.2020.011 Turci, L. F. R Pamplin, P. A. Z https://orcid.org/0000-0001-7516-0963 https://orcid.org/0000-0001-7318-9121 O estudo de plantas aquáticas (macrófitas) é importante, uma vez que essas plantas apresentam potencial de utilização em estudos de ecotoxicologia, como bioindicadores no tratamento de águas residuárias. A mode- lagem criteriosa do crescimento dessas plantas, especificamente a Lemna minor, é útil na determinação das condições de otimização dessas aplicações; assim, deseja-se sempre obter o modelo que melhor represente a dinâmica de crescimento populacional da planta em estudo. Neste trabalho, apresenta-se uma metodologia de ajuste e seleção de modelos de crescimento não lineares com base em indicadores estatísticos que servem como avaliadores de qualidade dos modelos. Para ilustrar o uso da metodologia, foi feito o cultivo de Lemna minor em meio Steinberg e foram ajustados três modelos aos dados médios de crescimento de suas frondes, selecionando o modelo Logístico como o melhor. Palavras-chave: Modelo de crescimento populacional. Avaliadores de qualidade. Lemna minor. Abstract The study of aquatic plants (macrophytes) is important since such plants present a potential utilization in ecotoxi- cology as bioindicators, as well in wastewater treatment. The criterious growth modelling of such plants, specifically Lemna minor, is useful for the determination of the optimal conditions of mentionedin applications - so one always looks for the best model that represents the dynamic of population growth of the plant in study. This work presents a methodology of adjustment and selection of nonlinear growth models based on statistical indicators, which work as quality evaluators for the models. To illustrate this methodology, Lemna minor was grown in Steinberg environ- ment, and three models were fitted to the fronds growth data, the Logistic model was selected as the best model. Keywords: Population growth model. Quality evaluators. Lemna minor.

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A Bayesian Non-parametric Mixed-Effects Model of Microbial Phenotypes

10.1101/793174 ◽

2019 ◽

Author(s):

Peter D. Tonner ◽

Cynthia L. Darnell ◽

Francesca M.L. Bushell ◽

Peter A. Lund ◽

Amy K. Schmid ◽

...

Keyword(s):

Population Growth ◽

Random Effects ◽

Growth Conditions ◽

Growth Behavior ◽

Parametric Models ◽

Growth Data ◽

Latent Growth ◽

Growth Curve Models ◽

Microbial Response ◽

Non Parametric

AbstractSubstantive changes in gene expression, metabolism, and the proteome are manifested in overall changes in microbial population growth. Quantifying how microbes grow is therefore fundamental to areas such as genetics, bioengineering, and food safety. Traditional parametric growth curve models capture the population growth behavior through a set of summarizing parameters. However, estimation of these parameters from data is confounded by random effects such as experimental variability, batch effects or differences in experimental material. A systematic statistical method to identify and correct for such confounding effects in population growth data is not currently available. Further, our previous work has demonstrated that parametric models are insufficient to explain and predict microbial response under non-standard growth conditions. Here we develop a hierarchical Bayesian non-parametric model of population growth that identifies the latent growth behavior and response to perturbation, while simultaneously correcting for random effects in the data. This model enables more accurate estimates of the biological effect of interest, while better accounting for the uncertainty due to technical variation. Additionally, modeling hierarchical variation provides estimates of the relative impact of various confounding effects on measured population growth.

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Pengembangan Sistem Penyediaan Air Bersih dengan Metode Appropriate Technology di Kelurahan Mekar Sari Kecamatan Rambutan Pemko Tebing Tinggi

Jurnal Riset Hesti Medan Akper Kesdam I/BB Medan ◽

10.34008/jurhesti.v6i1.230 ◽

2021 ◽

Vol 6 (1) ◽

pp. 34

Author(s):

Ikhwansyah Isranuri ◽

Nur Asnah Sitohang

Keyword(s):

Population Growth ◽

Water Supply ◽

Community Service ◽

Supply System ◽

Water Supply System ◽

Appropriate Technology ◽

National Standard ◽

Clean Water ◽

Growth Data ◽

The Public

Planning for a clean water supply system is a community service program carried out by USU in collaboration with the Dinas Pemberdayaan Masyarakat dan Desa(PMD) of the Pemko Tebing Tinggi. The problem encountered is the unavailability of clean water. This condition can interfere with people's daily activities such as the need for households. The clean water supply system that is implemented is the application of appropriate technology in the sense that it is easy to operate and all components are easy to care for by the public. The purpose of this activity is to provide clean water that meets health requirements, namely colorless, tasteless and odorless. This system is also planned to provide clean water for the next few years. The projection of population growth is also a consideration, which is calculated based on the average population growth data. Based on the Indonesian National Standard (SNI), the need for clean water for a population with a population of 300 households with the household category is 120 liters per person per day, so the water requirement is 1.67 liters/second. The source of water is obtained by digging 15 meters deep by installing 15 concrete rings with a diameter of 80 cm and a height of 100 cm. Then a pump is installed to suck water and then it is pumped into a poly tank (capacity 2100 liters) storage tank which is located at a height of 5-6 meters. Before flowing from the tank to the pipe, the water is filtered using a filter. For this purpose, a piping installation complete with a valve and a float is designed to automatically close the pipe when the tank is full. The result of water from this system is clean water and suitable for consumption by residents.

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PEMETAAN JARINGAN DISTRIBUSI (SUTM) 20 kV DAN PERKIRAAN KEBUTUHAN LISTRIK DI KECAMATAN PAYUNG SEKAKI, KABUPATEN SOLOK

Elektron : Jurnal Ilmiah ◽

10.30630/eji.5.2.51 ◽

2013 ◽

Vol 5 (2) ◽

pp. 29-38

Author(s):

Surfa Yondri ◽

Witrionanda Witrionanda ◽

A Fadly

Keyword(s):

Economic Growth ◽

Population Growth ◽

Data Processing ◽

Estimation Method ◽

Mapping Method ◽

Growth Data ◽

Real Condition ◽

The Real ◽

Electricity Network ◽

Mapping Result

Payung Sekaki district is one of the area in Solok Region that needs the development of electricity network. To support its development plan, the real condition of Payung Sekaki district should be known. The mapping method is one of the way to know the already implemented electricity area.Based on the mapping result, it is known that the electricity network of SUTM 20 kV at Payung Sekaki district has been implemented in Sikrukam area and Supayang area and not yet in Aie Luo area. The estimation method of electricity needs at Payung Sekaki district is motivated to get information on its development based on economic and population growth. Data processing results using SPSS show that the eletricity needs at Payung Sekaki district from 2013 to 2014 will dramatically increase followed by the increasing of economic growth in spite of population growth is still remain the same.

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Fast and Robust Parameter Estimation in the Application of Fuzzy Logistic Equations in Population Growth

MATEMATIKA ◽

10.11113/matematika.v35.n2.1164 ◽

2019 ◽

Vol 35 (2) ◽

pp. 249-259

Author(s):

Nor Atirah Izzah ◽

Yeak Su Hoe ◽

Normah Maan

Keyword(s):

Parameter Estimation ◽

Population Growth ◽

Kutta Method ◽

Growth Data ◽

Logistic Equations ◽

Runge Kutta Method ◽

Robust Parameter ◽

Runge Kutta ◽

Fourth Order Method ◽

Robust Parameter Estimation

In this paper, extended Runge-Kutta fourth order method for directly solving the fuzzy logistic problem is presented. The extended Runge-Kutta method has lower number of function evaluations, compared with the classical Runge-Kutta method. The numerical robustness of the method in parameter estimation is enhanced via error minimization in predicting growth rate and carrying capacity. The results of fuzzy logistic model with the estimated parameters have been compared with population growth data in Malaysia, which indicate that this method is more accurate that the data population. Numerical example is given to illustrate the efficiency of the proposed model. It is concluded that robust parameter estimation technique is efficient in modelling population growth.

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A Bayesian non-parametric mixed-effects model of microbial growth curves

PLoS Computational Biology ◽

10.1371/journal.pcbi.1008366 ◽

2020 ◽

Vol 16 (10) ◽

pp. e1008366

Author(s):

Peter D. Tonner ◽

Cynthia L. Darnell ◽

Francesca M. L. Bushell ◽

Peter A. Lund ◽

Amy K. Schmid ◽

...

Keyword(s):

Population Growth ◽

Random Effects ◽

Growth Curves ◽

Growth Conditions ◽

Growth Behavior ◽

Parametric Models ◽

Growth Data ◽

Growth Curve Models ◽

Microbial Response ◽

Non Parametric

Substantive changes in gene expression, metabolism, and the proteome are manifested in overall changes in microbial population growth. Quantifying how microbes grow is therefore fundamental to areas such as genetics, bioengineering, and food safety. Traditional parametric growth curve models capture the population growth behavior through a set of summarizing parameters. However, estimation of these parameters from data is confounded by random effects such as experimental variability, batch effects or differences in experimental material. A systematic statistical method to identify and correct for such confounding effects in population growth data is not currently available. Further, our previous work has demonstrated that parametric models are insufficient to explain and predict microbial response under non-standard growth conditions. Here we develop a hierarchical Bayesian non-parametric model of population growth that identifies the latent growth behavior and response to perturbation, while simultaneously correcting for random effects in the data. This model enables more accurate estimates of the biological effect of interest, while better accounting for the uncertainty due to technical variation. Additionally, modeling hierarchical variation provides estimates of the relative impact of various confounding effects on measured population growth.

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Detecting differential growth of microbial populations with Gaussian process regression

10.1101/055186 ◽

2016 ◽

Author(s):

Peter D Tonner ◽

Cynthia L Darnell ◽

Barbara E Engelhardt ◽

Amy K Schmid

Keyword(s):

Population Growth ◽

Gaussian Process ◽

Microbial Growth ◽

Microbial Population ◽

Statistical Tests ◽

Growth Curves ◽

Biological Knowledge ◽

Growth Data ◽

Differential Growth ◽

Differential Effects

AbstractMicrobial growth curves are used to study differential effects of media, genetics, and stress on microbial population growth. Consequently, many modeling frameworks exist to capture microbial population growth measurements. However, current models are designed to quantify growth under conditions that produce a specific functional form. Extensions to these models are required to quantify the effects of perturbations, which often exhibit non-standard growth curves. Rather than fix expected functional forms of different experimental perturbations, we developed a general and robust model of microbial population growth curves using Gaussian process (GP) regression. GP regression modeling of high resolution time-series growth data enables accurate quantification of population growth, and can be extended to identify differential growth phenotypes due to genetic background or stress. Additionally, confounding effects due to experimental variation can be controlled explicitly. Our framework substantially outperforms commonly used microbial population growth models, particularly when modeling growth data from environmentally stressed populations. We apply the GP growth model to a collection of growth measurements for seven transcription factor knockout strains of a model archaeal organism,Halobacterium salinarum. Using these models fitted to growth data, two statistical tests were developed to quantify the differential effects of genetic and environmental perturbations on microbial growth. These statistical tests accurately identify known regulators and implicate novel regulators of growth under standard and stress conditions. Furthermore, the fitted GP regression models are interpretable, recapitulating biological knowledge of growth response while providing new insights into the relevant parameters affecting microbial population growth.

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