scholarly journals Using Sigmoid Growth Curves to Establish Growth Models of Tomato and Eggplant Stems Suitable for Grafting in Subtropical Countries

Horticulturae ◽  
2021 ◽  
Vol 7 (12) ◽  
pp. 537
Author(s):  
Chih-Yu Hsieh ◽  
Shih-Lun Fang ◽  
Yea-Fang Wu ◽  
Yung-Chu Chu ◽  
Bo-Jein Kuo
Keyword(s):  
Seedling Growth ◽  
Growth Models ◽  
Solanum Melongena ◽  
Growth Curves ◽  
Model Parameters ◽  
Growth Data ◽  
Growing Seasons ◽  
Different Seasons ◽  
Prime Objective ◽  
Autumn And Winter

In subtropical regions, tomato (Solanum lycopersicum) is mainly produced in autumn and winter. To enhance the off-season production of tomato, summer cultivation has become a prime objective. Grafting tomato scions onto eggplant (Solanum melongena) rootstocks is a key method to overcome the difficulties of tomato cultivation in summer. In this study, we collected seedling growth data over six growing seasons in Taiwan and established growth models by employing three commonly used sigmoid growth curves, namely the Gompertz, Richards, and Logistic curves. Cumulative temperature was introduced as an independent variable and its relationship with plant stem diameter determined. The R2 values of the growth models were 0.74–0.85 and 0.72–0.80 in calibration and validation, respectively. Performance did not differ markedly among models in the same growing season, but notable differences were observed among models for different growing seasons. In addition, the estimates of several model parameters differed significantly among the seasons; hence, separate models should be established for different seasons. The results of this study can be used in prediction of tomato and eggplant seedling growth and arrangement of the grafting schedule to improve the efficiency of seedling production in subtropical countries.

Continuum versus discrete model: a comparison for multicellular tumour spheroids

2006 ◽  
Vol 364 (1843) ◽  
pp. 1443-1464 ◽  
Author(s):  
Gernot Schaller ◽  
Michael Meyer-Hermann
Keyword(s):  
Growth Curves ◽  
Cell Number ◽  
Nutrient Concentrations ◽  
Model Parameters ◽  
Agent Based Model ◽  
Growth Data ◽  
Agent Based ◽  
Pde Model ◽  
Viable Cells ◽  
Tumour Spheroids

We study multicellular tumour spheroids with a continuum model based on partial differential equations (PDEs). The model includes viable and necrotic cell densities, as well as oxygen and glucose concentrations. Viable cells consume nutrients and become necrotic below critical nutrient concentrations. Proliferation of viable cells is contact-inhibited if the total cellular density locally exceeds volume carrying capacity. The model is discussed under the assumption of spherical symmetry. Unknown model parameters are determined by simultaneously fitting the cell number to several experimental growth curves for different nutrient concentrations. The outcome of the PDE model is compared with an analogous off-lattice agent-based model for tumour growth. It turns out that the numerically more efficient PDE model suffices to explain the macroscopic growth data. As in the agent-based model, we find that the experimental growth curves are only reproduced when a necrotic core develops. However, evaluation of morphometric properties yields differences between the models and the experiment.

Dynamic child growth prediction: A comparative methods approach

2017 ◽  
Vol 17 (6) ◽  
pp. 468-493 ◽  
Author(s):  
Andrada E Ivanescu ◽  
Ciprian M Crainiceanu ◽  
William Checkley
Keyword(s):  
Regression Models ◽  
Prediction Models ◽  
Growth Curves ◽  
Child Growth ◽  
Prediction Performance ◽  
Model Parameters ◽  
Supplementary File ◽  
Functional Regression ◽  
Growth Data ◽  
Missing Data Imputation

Abstract: We introduce a class of dynamic regression models designed to predict the future of growth curves based on their historical dynamics. This class of models incorporates both baseline and time-dependent covariates, start with simple regression models and build up to dynamic function-on-function regressions. We compare the performance of the dynamic prediction models in a variety of signal-to-noise scenarios and provide practical solutions for model selection. We conclude that (a) prediction performance increases substantially when using the entire growth history relative to using only the last and first observation; (b) smoothing incorporated using functional regression approaches increases prediction performance; and (c) the interpretation of model parameters is substantially improved using functional regression approaches. Because many growth curve datasets exhibit missing and noisy data, we propose a bootstrap of subjects approach to account for the variability associated with the missing data imputation and smoothing. Methods are motivated by and applied to the CONTENT dataset, a study that collected monthly child growth data on 197 children from birth until month 15. R code describing the fitting approaches is provided in a supplementary file.

scholarly journals Comparison of Two Mechanistic Microbial Growth Models to Estimate Shelf Life of Perishable Food Package under Dynamic Temperature Conditions

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Dong Sun Lee
Keyword(s):  
Shelf Life ◽  
Microbial Growth ◽  
Electronic Device ◽  
Growth Models ◽  
Model Parameters ◽  
Growth Data ◽  
Dynamic Temperature ◽  
Temperature Conditions ◽  
Primary Model ◽  
Set Up

Two mechanistic microbial growth models (Huang’s model and model of Baranyi and Roberts) given in differential and integrated equation forms were compared in predicting the microbial growth and shelf life under dynamic temperature storage and distribution conditions. Literatures consistently reporting the microbial growth data under constant and changing temperature conditions were selected to obtain the primary model parameters, set up the secondary models, and apply them to predict the microbial growth and shelf life under fluctuating temperatures. When evaluated by general estimation behavior, bias factor, accuracy factor, and root-mean-square error, Huang’s model was comparable to Baranyi and Roberts’ model in the capability to estimate microbial growth under dynamic temperature conditions. Its simple form of single differential equation incorporating directly the growth rate and lag time may work as an advantage to be used in online shelf life estimation by using the electronic device.

scholarly journals Dynamic Top Height Growth Models for Eight Native Tree Species in a Cool-Temperate Region in Northeast China

Forests ◽  
2021 ◽  
Vol 12 (8) ◽  
pp. 965
Author(s):  
Sandra-Maria Hipler ◽  
Heinrich Spiecker ◽  
Shuirong Wu
Keyword(s):  
Tree Species ◽  
Northeast China ◽  
Growth Models ◽  
Growth Curves ◽  
Height Growth ◽  
Shoot Length ◽  
Growth Data ◽  
Annual Shoot ◽  
Picea Asperata ◽  
Length Measurements

In this study, we developed dynamic top height growth models for the eight important Chinese tree species Larix gmelinii var. principis-rupprechtii, Pinus tabuliformis Carr., Pinus sylvestris var. mongolica Litv., Picea asperata Mast., Quercus mongolica Fisch. ex Ledeb, Betula platyphylla Suk., Betula dahurica Pall. and Populus davidiana Dode based on age-height relationships. For this purpose, commonly growth data from long-term observations of permanent experimental plots are used, which ideally cover all development stages from stand establishment to final harvest. As such data were not available in the research area of Hebei Province in Northeast China, we used stem analysis data as well as tree height and annual shoot length measurements. The dataset consisted of 72 stands, 233 dominant trees and 10,195 observations of stem discs and annual shoot length measurements. Five dynamic base-age invariant top height growth models were derived from four base models with the Generalized Algebraic Difference Approach and fitted to our age-height data using nested regression techniques. According to biological plausibility and model accuracy the Chapman–Richards model showed the best performance for Picea asperata. This selected model accounted for 99% of the total variance in age-height relationship with average absolute bias of 0.2322 m, root mean square error of 0.3337 m and of 0.9979, respectively. The distribution of the residuals was scattered around 0 and without visible trends, indicating that the fitness of the models was good. All developed models are able to generate top height growth curves representing the analyzed height growth data and can be utilized for predicting height growth on the base of current height and age of dominant trees. Additionally, they are the base for calculating the development of other relevant stand attributes such as basal area and volume growth. The determination of potential site productivity by the use of top height growth curves is a practical and convenient method for a simplified presentation of complex growth processes in stands and helps to create growth models, which facilitate implementing sustainable forest management practices in Mulan Forest.

scholarly journals Multiphasic growth models for cattle

2011 ◽  
Vol 50 (No. 8) ◽  
pp. 347-354 ◽  
Author(s):  
H. Nešetřilová
Keyword(s):  
Growth Models ◽  
Gompertz Model ◽  
Logistic Function ◽  
Complex Nature ◽  
Model Parameters ◽  
Growth Data ◽  
Unknown Parameters ◽  
Residual Variability ◽  
Low Degree ◽  
Best Fitting

There are several ways of generalizing classical growth models to describe the complex nature of animal growth. One possibility is to construct a model based on a sum of several classical growth functions. In this paper, such multiphasic growth models for breeding bulls of the Czech Pied cattle based on the sum of two logistic functions are studied. The logistic function was chosen as a base for the models due to the relatively low degree of nonlinearity for the growth data. The paper describes three steps of constructing such a multiphasic growth model: in the first step a model with four unknown parameters is considered, in the second step the number of model parameters which are to be estimated is increased to five and in the third step a general model with six parameters is used. In each step, statistical properties of the considered model are checked. The residual variability of the best fitting model is on average approx. 8 times lower than the residual variability of classical Gompertz model which is often used by breeders to model cattle growth.  

scholarly journals Modeling Software Fault-Detection and Fault-Correction Processes by Considering the Dependencies between Fault Amounts

2021 ◽  
Vol 11 (15) ◽  
pp. 6998
Author(s):  
Qiuying Li ◽  
Hoang Pham
Keyword(s):  
Time Delay ◽  
Fault Detection ◽  
Software Reliability ◽  
Growth Models ◽  
Predictive Performance ◽  
Real Data ◽  
Reliability Estimation ◽  
Model Parameters ◽  
Dual Processes ◽  
Correction Process

Many NHPP software reliability growth models (SRGMs) have been proposed to assess software reliability during the past 40 years, but most of them have focused on modeling the fault detection process (FDP) in two ways: one is to ignore the fault correction process (FCP), i.e., faults are assumed to be instantaneously removed after the failure caused by the faults is detected. However, in real software development, it is not always reliable as fault removal usually needs time, i.e., the faults causing failures cannot always be removed at once and the detected failures will become more and more difficult to correct as testing progresses. Another way to model the fault correction process is to consider the time delay between the fault detection and fault correction. The time delay has been assumed to be constant and function dependent on time or random variables following some kind of distribution. In this paper, some useful approaches to the modeling of dual fault detection and correction processes are discussed. The dependencies between fault amounts of dual processes are considered instead of fault correction time-delay. A model aiming to integrate fault-detection processes and fault-correction processes, along with the incorporation of a fault introduction rate and testing coverage rate into the software reliability evaluation is proposed. The model parameters are estimated using the Least Squares Estimation (LSE) method. The descriptive and predictive performance of this proposed model and other existing NHPP SRGMs are investigated by using three real data-sets based on four criteria, respectively. The results show that the new model can be significantly effective in yielding better reliability estimation and prediction.

scholarly journals Age and growth of franciscana dolphins, Pontoporia blainvillei (Cetacea: Pontoporiidae) incidentally caught off southern Brazil and northern Argentina

2010 ◽  
Vol 90 (8) ◽  
pp. 1493-1500 ◽  
Author(s):  
Silvina Botta ◽  
Eduardo R. Secchi ◽  
Mônica M.C. Muelbert ◽  
Daniel Danilewicz ◽  
Maria Fernanda Negri ◽  
...  
Keyword(s):  
Growth Models ◽  
Growth Curves ◽  
Small Sample ◽  
Age And Growth ◽  
Buenos Aires Province ◽  
Von Bertalanffy ◽  
Southern Brazil ◽  
Data Set ◽  
Males And Females ◽  
Pontoporia Blainvillei

Age and length data of 291 franciscana dolphins (Pontoporia blainvillei) incidentally captured on the coast of Rio Grande do Sul State (RS), southern Brazil, were used to fit growth curves using Gompertz and Von Bertalanffy growth models. A small sample of franciscanas (N = 35) from Buenos Aires Province (BA), Argentina, were used to see if there are apparent growth differences between the populations. Male and female franciscana samples from both areas were primarily (78–85%) <4 years of age. The Von Bertalanffy growth model with a data set that excluded animals <1 year of age provided the best fit to data. Based on this model, dolphins from the RS population reached asymptotic length at 136.0 cm and 158.4 cm, for males and females, respectively. No remarkable differences were observed in the growth trajectories of males and females between the RS and BA populations.

Dynamic Predictive Model for Growth of Bacillus cereus from Spores in Cooked Beans

2017 ◽  
Vol 81 (2) ◽  
pp. 308-315 ◽  
Author(s):  
Vijay K. Juneja ◽  
Abhinav Mishra ◽  
Abani K. Pradhan
Keyword(s):  
Bacillus Cereus ◽  
Growth Rates ◽  
Linear Models ◽  
Growth Models ◽  
Maximum Growth ◽  
Effect Of Temperature ◽  
Coefficient Of Determination ◽  
Growth Data ◽  
Three Phase ◽  
Primary Model

ABSTRACT Kinetic growth data for Bacillus cereus grown from spores were collected in cooked beans under several isothermal conditions (10 to 49°C). Samples were inoculated with approximately 2 log CFU/g heat-shocked (80°C for 10 min) spores and stored at isothermal temperatures. B. cereus populations were determined at appropriate intervals by plating on mannitol–egg yolk–polymyxin agar and incubating at 30°C for 24 h. Data were fitted into Baranyi, Huang, modified Gompertz, and three-phase linear primary growth models. All four models were fitted to the experimental growth data collected at 13 to 46°C. Performances of these models were evaluated based on accuracy and bias factors, the coefficient of determination (R2), and the root mean square error. Based on these criteria, the Baranyi model best described the growth data, followed by the Huang, modified Gompertz, and three-phase linear models. The maximum growth rates of each primary model were fitted as a function of temperature using the modified Ratkowsky model. The high R2 values (0.95 to 0.98) indicate that the modified Ratkowsky model can be used to describe the effect of temperature on the growth rates for all four primary models. The acceptable prediction zone (APZ) approach also was used for validation of the model with observed data collected during single and two-step dynamic cooling temperature protocols. When the predictions using the Baranyi model were compared with the observed data using the APZ analysis, all 24 observations for the exponential single rate cooling were within the APZ, which was set between −0.5 and 1 log CFU/g; 26 of 28 predictions for the two-step cooling profiles also were within the APZ limits. The developed dynamic model can be used to predict potential B. cereus growth from spores in beans under various temperature conditions or during extended chilling of cooked beans.

scholarly journals Temporal–Spatial Patterns of Relative Humidity and the Urban Dryness Island Effect in Beijing City

2017 ◽  
Vol 56 (8) ◽  
pp. 2221-2237 ◽  
Author(s):  
Ping Yang ◽  
Guoyu Ren ◽  
Wei Hou
Keyword(s):  
Relative Humidity ◽  
Urban Areas ◽  
Late Summer ◽  
Beijing City ◽  
Near Surface ◽  
Suburban Areas ◽  
Island Effect ◽  
Different Seasons ◽  
Autumn And Winter ◽  
Beijing China

AbstractHourly datasets obtained by automatic weather stations in Beijing, China, are developed and employed to analyze the spatial and temporal characteristics of relative humidity (RH) and urban dryness island intensity (UDII) over built-up areas. A total of 36 stations inside the sixth ring road are considered as urban sites, while six stations in suburban belts surrounding the built-up areas are taken as reference sites. Results show that the RH is obviously smaller in urban areas than in suburban areas, indicating the effect of urbanization on near-surface atmospheric moisture and RH. A further analysis of relations between RH and temperature on varied time scales shows that the variations in RH in the urban areas are not due solely to changes in temperature. The annual and seasonal mean UDII are high in central urban areas, with the strongest UDII values occurring in autumn and the weakest values occurring in spring. The diurnal UDII variations are characterized by a steadily strong UDII stage from 2000 to 0800 LT and a minimum at 1500 or 1600 LT. The rapid shifts of UDII from high (low) to low (high) occur during the periods 0800–1600 LT (1600–2000 LT). The occurrence time of the peaks varies among different seasons: the peaks appear at 0700, 2100, 2000, and 0800 LT for spring, summer, autumn, and winter, respectively. Further analysis shows that large UDII values appear in the evenings and early nights in late summer and early to midautumn and that low UDII values mainly occur in the afternoon hours of spring, winter, and late autumn.

scholarly journals Investigation of Cryptosporidium spp. and Giardia spp. in Lake Eğirdir (Isparta)

2021 ◽  
Author(s):  
Tuğba Sağlam ◽  
Serdar Düşen ◽  
Meral Apaydın Yağcı ◽  
Abdülkadir Yağcı
Keyword(s):  
Public Health ◽  
Drinking Water ◽  
Water Samples ◽  
Average Density ◽  
Agricultural Irrigation ◽  
Control Programs ◽  
Cryptosporidium Spp ◽  
Direct Microscopic Examination ◽  
Different Seasons ◽  
Autumn And Winter

Objective: The aim of this study was to assess both the presence and seasonal variability of Cryptosporidium spp. and Giardia spp. in Eğirdir Lake within the borders of Isparta province, which is used for drinking, agricultural irrigation and recreational purposes. Method: The research was carried out between July 2016 and January 2017 and water samples were taken from five different stations in three different seasons in Lake Eğirdir. After direct microscopic examination of the samples (Native-Lugol method), they were stained with Modified Acid Fast (MAF), and examined under the light microscope for parasites. Results: Cryptosporidium spp and Giardia spp were detected in 15 water samples in summer months, with an average density of 99.2% and 93.3% respectively, in Lake Eğirdir. In addition, both parasites were also detected intensively in autumn and winter Conclusion: The use of Lake Eğirdir for daily needs of people, agriculture andrecreational purposes cause increase in protozoal density. Thus, it is necessary to conduct parasitological studies on Lake Eğirdir, especially during the periods of swimming tourism, to determine the protozoal epidemiology in humans and animals. In addition, it is important to carry out adequate disinfection processes and plan the necessary control programs in terms of public health in the regions where Lake Eğirdir is used as drinking water.

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