scholarly journals Predicting Fermentation Rates in Ale, Lager and Whisky

Fermentation ◽  
2021 ◽  
Vol 7 (1) ◽  
pp. 13
Author(s):  
Struan J. Reid ◽  
Maria Josey ◽  
Andrew J. MacIntosh ◽  
Dawn L. Maskell ◽  
R. Alex Speers
Keyword(s):  
Logistic Model ◽  
Goodness Of Fit ◽  
Information Criterion ◽  
Logistic Function ◽  
Logistic Equation ◽  
Additional Parameter ◽  
Final Model ◽  
Industrial Fermentation ◽  
Scotch Whisky ◽  
Extra Parameter

Recently there has been an increased interest in characterising the rates of alcoholic fermentations. Sigmoidal models have been used to predict changes such as the rate of density decline. In this study, three published sigmoidal models were assessed and fit to industrial fermentation data. The first is the four-parameter logistic model described in the ASBC Yeast-14 method. The second model is a nested form of the four-parameter logistic function, adding an extra parameter, creating the 5-parameter logistic equation., where an additional parameter was added to allow for asymmetry. The final model is a three-parameter logistic equation which describes the change in the Apparent Degree of Fermentation with time. The three models were compared by fitting them to industrial data from Australian and Canadian lagers, American and Scottish ales and Scotch Whisky fermentations. The model fits were then compared to one another with a technique developed by Akaike and a nested F-test. The Akaike information criterion compares the models and accounts for both the goodness of fit, and the number of parameters in the model. Finally, consideration was given to the establishment of prediction bands (that enclose the area that one can be 99% sure contains the true datapoints). Calculation of these bands was “challenging” but successful as the industrial fermentation data was heteroscedastic.

Using double logistic equation to describe the growth of winter rapeseed

2018 ◽  
Vol 156 (1) ◽  
pp. 37-45 ◽  
Author(s):  
A. Shabani ◽  
A. R. Sepaskhah ◽  
A. A. Kamgar-Haghighi ◽  
T. Honar
Keyword(s):  
Growth Rate ◽  
Water Stress ◽  
Goodness Of Fit ◽  
Grain Filling ◽  
Maximum Growth ◽  
Logistic Function ◽  
Logistic Equation ◽  
Cold Period ◽  
Growth Stages ◽  
Winter Rapeseed

AbstractThere are many parameters in agriculture that change over time in a sigmoid pattern. In the current study, the double logistic function was used to describe and simulate dry matter (DM) variation of winter rapeseed plant and to explain the growth rate under water stress. Irrigation treatments were full irrigation at all growth stages, water stress during the vegetative stage in early spring, water stress at flowering and podding stages, water stress at grain filling stage and rain-fed treatment with supplemental irrigation at time of planting. A high value for the goodness of fit (0.996) and low value for normalized root mean square error (0.085) showed that the double logistic function can describe and simulate DM variation of rapeseed accurately. DM predicted by the double logistic equation based on growing degree day was slightly closer to the measured DM compared with the DM predicted by the double logistic equation based on days after planting. Results showed that growth rate before the winter cold period was lower than that after this period. There were two maximum growth rates for winter rapeseed: the first occurred before cold period and another after.

A Goodness of Fit Test for the Multilevel Logistic Model

2015 ◽  
Vol 45 (2) ◽  
pp. 643-659 ◽  
Author(s):  
A. A. P. N. M. Perera ◽  
M. R. Sooriyarachchi ◽  
S. L. Wickramasuriya
Keyword(s):  
Logistic Model ◽  
Goodness Of Fit ◽  
Goodness Of Fit Test ◽  
Multilevel Logistic Model

scholarly journals Comparison of growth models to describe growth from birth to 6 years in a Beninese cohort of children with repeated measurements

BMJ Open ◽  
2020 ◽  
Vol 10 (9) ◽  
pp. e035785
Author(s):  
Shukrullah Ahmadi ◽  
Florence Bodeau-Livinec ◽  
Roméo Zoumenou ◽  
André Garcia ◽  
David Courtin ◽  
...  
Keyword(s):  
Growth Model ◽  
Goodness Of Fit ◽  
Growth Models ◽  
Information Criterion ◽  
Height Growth ◽  
Sub Saharan Africa ◽  
Individual Growth ◽  
Secondary Outcome ◽  
Growth Trajectories ◽  
Best Fitting

ObjectiveTo select a growth model that best describes individual growth trajectories of children and to present some growth characteristics of this population.SettingsParticipants were selected from a prospective cohort conducted in three health centres (Allada, Sekou and Attogon) in a semirural region of Benin, sub-Saharan Africa.ParticipantsChildren aged 0 to 6 years were recruited in a cohort study with at least two valid height and weight measurements included (n=961).Primary and secondary outcome measuresThis study compared the goodness-of-fit of three structural growth models (Jenss-Bayley, Reed and a newly adapted version of the Gompertz growth model) on longitudinal weight and height growth data of boys and girls. The goodness-of-fit of the models was assessed using residual distribution over age and compared with the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). The best-fitting model allowed estimating mean weight and height growth trajectories, individual growth and growth velocities. Underweight, stunting and wasting were also estimated at age 6 years.ResultsThe three models were able to fit well both weight and height data. The Jenss-Bayley model presented the best fit for weight and height, both in boys and girls. Mean height growth trajectories were identical in shape and direction for boys and girls while the mean weight growth curve of girls fell slightly below the curve of boys after neonatal life. Finally, 35%, 27.7% and 8% of boys; and 34%, 38.4% and 4% of girls were estimated to be underweight, wasted and stunted at age 6 years, respectively.ConclusionThe growth parameters of the best-fitting Jenss-Bayley model can be used to describe growth trajectories and study their determinants.

scholarly journals The use of the nonlinear models in the growth of pears of ‘Shinseiki’ cultivar

2017 ◽  
Vol 48 (1) ◽  
Author(s):  
Thais Destefani Ribeiro ◽  
Taciana Villela Savian ◽  
Tales Jesus Fernandes ◽  
Joel Augusto Muniz
Keyword(s):  
Logistic Model ◽  
Least Squares Method ◽  
Nonlinear Models ◽  
Logistic Models ◽  
Information Criterion ◽  
Fruit Weight ◽  
Coefficient Of Determination ◽  
Pear Fruit ◽  
Asian Pear ◽  
Residual Correlations

ABSTRACT: The goal of this study was to elucidate the growth and development of the Asian pear fruit, on the grounds of length, diameter and fresh weight determined over time, using the non-linear Gompertz and Logistic models. The specifications of the models were assessed utilizing the R statistical software, via the least squares method and iterative Gauss-Newton process (DRAPER & SMITH, 2014). The residual standard deviation, adjusted coefficient of determination and the Akaike information criterion were used to compare the models. The residual correlations, observed in the data for length and diameter, were modeled using the second-order regression process to render the residuals independent. The logistic model was highly suitable in demonstrating the data, revealing the Asian pear fruit growth to be sigmoid in shape, showing remarkable development for three variables. It showed an average of up to 125 days for length and diameter and 140 days for fresh fruit weight, with values of 72mm length, 80mm diameter and 224g heavy fat.

Pharmacokinetics of Ceftolozane-Tazobactam during Prolonged Intermittent Renal Replacement Therapy

Chemotherapy ◽  
2018 ◽  
Vol 63 (4) ◽  
pp. 203-206 ◽  
Author(s):  
Matthew Rawlins ◽  
Vesa Cheng ◽  
Edward Raby ◽  
John Dyer ◽  
Adrian Regli ◽  
...  
Keyword(s):  
Renal Replacement Therapy ◽  
Replacement Therapy ◽  
Goodness Of Fit ◽  
Final Model ◽  
Blood Samples ◽  
Compartment Models ◽  
Log Likelihood ◽  
New Antibiotics ◽  
Plasma Pharmacokinetics ◽  
Intermittent Renal Replacement Therapy

Background: Prolonged intermittent renal replacement therapy (PIRRT) eliminates many drugs, and without dosing data, for new antibiotics like ceftolozane/tazobactam, suboptimal concentrations and treatment failure are likely. Objectives: Herein, we describe the effect of PIRRT on the plasma pharmacokinetics of ceftolozane/tazobactam ad­ministered in a critically ill 55-year-old patient with a polymicrobial sternal wound osteomyelitis, including a multiresistant Pseudomonas aeruginosa. Method: Blood samples were taken over 4 days where the patient received a 7.5-h PIRRT treatment. One- and 2-compartment models were tested for ceftolozane and tazobactam separately, and the log-likelihood ratio and goodness-of-fit plots were used to select the final model. Results: Two-compartment models were developed for ceftolozane and tazobactam separately and described significant differences in clearance of ceftolozane and tazobactam with and without PIRRT (8.273 vs. 0.393 and 8.020 vs. 0.767 L/h, respectively). Conclusions: A ceftolozane/tazobactam dose of 500 mg/250 mg appears to be sufficient to attain pharmacokinetic/pharmacodynamic targets during PIRRT while the manufacturer’s recommended dosing of 100 mg/50 mg every 8 h was sufficient during non-PIRRT periods.

Grey Logistic Model to Predict the Effective Irrigation Area in Liaoning Province

2014 ◽  
Vol 641-642 ◽  
pp. 179-182 ◽  
Author(s):  
Jin Liang Chen
Keyword(s):  
Logistic Model ◽  
Structural Adjustment ◽  
Logistic Equation ◽  
Liaoning Province ◽  
Forecasting Model ◽  
Irrigation Area ◽  
Agricultural Resources ◽  
Upper Limit ◽  
Simulation Results ◽  
Good Agreement

Through grey estimation of the parameters of logistic equation, a grey logistic forecasting model is established. The effective irrigation area in Liaoning Province was simulated by the model. The simulation results had good agreement with the available data, with a correlation of 0.95. The effective irrigation area was predicted to be 1.583 million hectares in 2018, very close to the predicted upper limit of 1.588 million hectares. Thus, there is little potential for the development of the effective irrigation area, rendering the structural adjustment of agricultural resources very necessary.

scholarly journals Applicability of Item Response Theory to the Korean Nurses' Licensing Examination

2005 ◽  
Vol 2 ◽  
pp. 23 ◽  
Author(s):  
Geum-Hee Jeong ◽  
Mi Kyoung Yim
Keyword(s):  
Rasch Model ◽  
Logistic Model ◽  
Goodness Of Fit ◽  
Item Analysis ◽  
Classical Test Theory ◽  
Test Theory ◽  
Classical Test ◽  
Licensing Examination ◽  
The Rasch Model ◽  
Two Parameter

To test the applicability of item response theory (IRT) to the Korean Nurses' Licensing Examination (KNLE), item analysis was performed after testing the unidimensionality and goodness-of-fit. The results were compared with those based on classical test theory. The results of the 330-item KNLE administered to 12,024 examinees in January 2004 were analyzed. Unidimensionality was tested using DETECT and the goodness-of-fit was tested using WINSTEPS for the Rasch model and Bilog-MG for the two-parameter logistic model. Item analysis and ability estimation were done using WINSTEPS. Using DETECT, Dmax ranged from 0.1 to 0.23 for each subject. The mean square value of the infit and outfit values of all items using WINSTEPS ranged from 0.1 to 1.5, except for one item in pediatric nursing, which scored 1.53. Of the 330 items, 218 (42.7%) were misfit using the two-parameter logistic model of Bilog-MG. The correlation coefficients between the difficulty parameter using the Rasch model and the difficulty index from classical test theory ranged from 0.9039 to 0.9699. The correlation between the ability parameter using the Rasch model and the total score from classical test theory ranged from 0.9776 to 0.9984. Therefore, the results of the KNLE fit unidimensionality and goodness-of-fit for the Rasch model. The KNLE should be a good sample for analysis according to the IRT Rasch model, so further research using IRT is possible.

scholarly journals Model Selection in Peptide-receptor Radionuclide Therapy for an Accurate Determination of Time Integrated Activity Coefficients

2021 ◽  
Vol 2019 (1) ◽  
pp. 012079
Author(s):  
N Atikah ◽  
A Riana ◽  
A Dwi ◽  
Z Anwari ◽  
Misrawati ◽  
...  
Keyword(s):  
Model Selection ◽  
Activity Coefficients ◽  
Radionuclide Therapy ◽  
Goodness Of Fit ◽  
Accurate Determination ◽  
Peptide Receptor Radionuclide Therapy ◽  
Information Criterion ◽  
Selection Function ◽  
Pharmacokinetic Data ◽  
Peptide Receptor

Abstract Calculation of accurate time-integrated activity coefficients (TIACs) is desirable in nuclear medicine dosimetry. The accuracy of the calculated TIACs is highly dependent on the fit function. However, systematic studies of determining a good function for peptide-receptor radionuclide therapy (PRRT) in different patients have not been reported in the literature. The aim of this study was to individually determine the best function for the calculation of TIACs in tumor and kidneys using a model selection based on the goodness of fit criteria and Corrected Akaike Information Criterion (AICc). The data used in this study was pharmacokinetic data of 111In-DOTATATE in tumor and kidneys obtained from 4 PRRT patients. Eleven functions with various parameterizations were formulated to describe the biokinetic data of 111In-DOTATATE in tumor and kidneys. The model selection was performed by choosing the best function from the function with sufficient goodness of fit based on the smallest AICc. Based on the results of model selection, function A 1 -(λ 1+λphys )t was selected as the best function for all tumor and kidneys in patients with meningioma tumors. By using this function, the calculated of TIACs could be more accurate for future patients with meningioma tumor.

scholarly journals Tornumonkpe Distribution: Statistical Properties and Goodness of Fit

2021 ◽  
pp. 12-22
Author(s):  
Barinaadaa John Nwikpe
Keyword(s):  
Goodness Of Fit ◽  
Real Life ◽  
Mathematical Expression ◽  
Information Criterion ◽  
Moment Generating Function ◽  
Data Sets ◽  
The Moment ◽  
The One ◽  
Coefficient Of Skewness ◽  
New Distribution

A new sole parameter probability distribution named the Tornumonkpe distribution has been derived in this paper. The new model is a blend of gamma (2,  and gamma(3  distributions. The shape of its density for different values of the parameter has been shown.  The mathematical expression for the moment generating function, the first three raw moments, the second and third moments about the mean, the distribution of order statistics, coefficient of variation and coefficient of skewness has been given. The parameter of the new distribution was estimated using the method of maximum likelihood. The goodness of fit of the Tornumonkpe distribution was established by fitting the distribution to three real life data sets. Using -2lnL, Bayesian Information Criterion (BIC), and Akaike Information Criterion(AIC) as criterial for selecting the best fitting model, it was revealed that the new distribution outperforms the one parameter exponential, Shanker and Amarendra distributions for the data sets used.

scholarly journals Longitudinal Pattern of Aerenchyma Formation Using the Ti-Gompertz Model in Rice Adventitious Roots

2021 ◽  
Vol 12 ◽  
Author(s):  
Yun Chen ◽  
Guoming Li ◽  
Buhong Zhao ◽  
Yajun Zhang ◽  
Kun Liu ◽  
...  
Keyword(s):  
Adventitious Roots ◽  
Goodness Of Fit ◽  
Gompertz Model ◽  
Root Apex ◽  
Information Criterion ◽  
Ordinary Least Squares ◽  
Parameter Estimates ◽  
Aerenchyma Formation ◽  
Root Aerenchyma ◽  
Longitudinal Pattern

The longitudinal pattern of root aerenchyma formation of its relationship with the function of adventitious roots in rice remains unclear. In this study, the percentage of the aerenchyma area to the cross-sectional area (i.e., aerenchyma percentage) was fit with four non-linear models, namely, W0-Gompertz, Ti-Gompertz, logistic, and von Bertalanffy. Goodness-of-fit criteria such as the R2, the Akaike information criterion (AIC), and the Bayesian information criterion (BIC) were used to select the model. The bias of the parameters was evaluated using the difference between the ordinary least squares-based parameter estimates and the mean of 1,000 bootstrap-based parameter estimates and the symmetry of the distributions of these parameters. The results showed that the Ti-Gompertz model, which had a high goodness-of-fit with an R2 close to 1, lower AIC and BIC values, parameter estimates close to being unbiased, and good linear approximation, provided the best fit for the longitude pattern of rice aerenchyma formation with different root lengths among the competing models. Using the second- and third-order derivatives according to the distance from the root apex, the critical points of Ti-Gompertz were calculated. The rapid stage for aerenchyma formation was from the maximum acceleration point (1.38–1.76 cm from the root apex) to the maximum deceleration point (3.13–4.19 cm from the root apex). In this stage, the aerenchyma percentage increased by 5.3–15.7% per cm, suggesting that the cortical cells tended to die rapidly for the aerenchyma formation rather than for the respiration cost during this stage. Meanwhile, the volume of the aerenchyma of the entire roots could be computed using the integral function of the Ti-Gompertz model. We proposed that the longitudinal pattern of root aerenchyma formation modeled by the Ti-Gompertz model helped to deeply understand the relationship between the anatomical traits and physiological function in rice adventitious roots.

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