scholarly journals Penalty Function Selection Method Of Best-Fit Model Parameters Of Interacting Agricultural Crops In Oil Uncontaminated Utisol:Part 1

2013 ◽  
Vol 7 (6) ◽  
pp. 100-105
Author(s):  
2008 ◽  
Vol 10 (2) ◽  
pp. 153-162 ◽  
Author(s):  
B. G. Ruessink

When a numerical model is to be used as a practical tool, its parameters should preferably be stable and consistent, that is, possess a small uncertainty and be time-invariant. Using data and predictions of alongshore mean currents flowing on a beach as a case study, this paper illustrates how parameter stability and consistency can be assessed using Markov chain Monte Carlo. Within a single calibration run, Markov chain Monte Carlo estimates the parameter posterior probability density function, its mode being the best-fit parameter set. Parameter stability is investigated by stepwise adding new data to a calibration run, while consistency is examined by calibrating the model on different datasets of equal length. The results for the present case study indicate that various tidal cycles with strong (say, >0.5 m/s) currents are required to obtain stable parameter estimates, and that the best-fit model parameters and the underlying posterior distribution are strongly time-varying. This inconsistent parameter behavior may reflect unresolved variability of the processes represented by the parameters, or may represent compensational behavior for temporal violations in specific model assumptions.


2021 ◽  
Author(s):  
Sansiddh Jain ◽  
Avtansh Tiwari ◽  
Nayana Bannur ◽  
Ayush Deva ◽  
Siddhant Shingi ◽  
...  

Forecasting infection case counts and estimating accurate epidemiological parameters are critical components of managing the response to a pandemic. This paper describes a modular, extensible framework for a COVID-19 forecasting system, primarily deployed in Mumbai and Jharkhand, India. We employ a variant of the SEIR compartmental model motivated by the nature of the available data and operational constraints. We estimate best-fit parameters using sequential Model-Based Optimization (SMBO) and describe the use of a novel, fast, and approximate Bayesian model averaging method (ABMA) for parameter uncertainty estimation that compares well with a more rigorous Markov Chain Monte Carlo (MCMC) approach in practice. We address on-the-ground deployment challenges such as spikes in the reported input data using a novel weighted smooth-ing method. We describe extensive empirical analyses to evaluate the accuracy of our method on ground truth as well as against other state-of-the-art approaches. Finally, we outline deployment lessons and describe how inferred model parameters were used by government partners to interpret the state of the epidemic and how model forecasts were used to estimate staffing and planning needs essential for addressing COVID-19 hospital burden.


2019 ◽  
Vol 19 (1) ◽  
pp. 86-92
Author(s):  
M. Owusu ◽  
H. Osei

Appropriate selection of rheological models is important for hydraulic calculations of pressure loss prediction and hole cleaning efficiency of drilling fluids. Power law, Bingham-Plastic and Herschel-Bulkley models are the conventional fluid models used in the oilfield. However, there are other models that have been proposed in literature which are under/or not utilized in the petroleum industry. The primary objective of this paper is to recommend a rheological model that best-fits the rheological behaviour of xanthan gum-based biopolymer drill-in fluids for hydraulic evaluations. Ten rheological models were evaluated in this study. These rheological models have been posed deterministically and due to the unrealistic nature have been replaced by statistical models, by adding an error (disturbance) term and making suitable assumptions about them. Rheological model parameters were estimated by least-square regression method. Models like Sisko and modified Sisko which are not conventional models in oil industry gave a good fit. Modified Sisko model which is a four parameter rheological model was selected as the best-fit model since it produced the least residual mean square of 0.61 Ibf2/100ft4. There is 95% certainty that the true best-fit curve lies within the confidence band of this function of interest. Keywords: Biopolymer; Least-Square Regression; Residual Mean Squares; Rheologram


2019 ◽  
Vol 97 (2) ◽  
pp. 117-124 ◽  
Author(s):  
M. Salti ◽  
O. Aydogdu ◽  
A. Tas ◽  
K. Sogut ◽  
E.E. Kangal

We investigate cosmological features of the variable Chaplygin gas (VCG) describing a unified dark matter–energy scenario in a universe governed by the five dimensional (5D) Kaluza–Klein (KK) gravity. In such a proposal, the VCG evolves from the dust-like phase to the phantom or the quintessence phases. It is concluded that the background evolution for the KK-type VCG definition is equivalent to that for the dark energy interacting with the dark matter. Next, after performing neo-classical tests, we calculated the proper, luminosity, and angular diameter distances. Additionally, we construct a connection between the VCG in the KK universe and a homogenous minimally coupled scalar field by introducing its self-interacting potential and also we confirm the stability of the KK-type VCG model by making use of thermodynamics. Moreover, we use data from type Ia supernova, observational H(z) dataset and Planck-2015 results to place constraints on the model parameters. Subsequently, according to the best-fit values of the model parameters we analyze our results numerically.


2020 ◽  
Author(s):  
HanFang Hsueh ◽  
Anneli Guthke ◽  
Eddy Thomas Woehling ◽  
Wolfgang Nowak

<p>When a deterministic hydrological model is calibrated, parameters applied in the model are commonly assigned time-constant values. This assignment ignores that errors in the model structure lead to time-dependent model errors. Such time-dependent error occurs, among other reasons, if a hydrological process is active in certain periods or situations in nature, yet is not captured by a model. Examples include soil freezing, complex vegetation dynamics, or the effect of extreme floods on river morphology. For a given model approximation, such missing process could become visible as apparent time-dependent best-fit values of model parameters. This research aims to develop a framework based on time-windowed Bayesian inference, to assist modelers in diagnosing this type of model error.</p><p><br>We suggest using time-windowed Bayesian model evidence (tBME) as a model evaluation metric, indicating how much the data in time windows support the claim that the model is correct. We will explain how to make tBME values a meaningful and comparable indicator within likelihood-ratio hypothesis tests. By using a sliding time window, the hypothesis test will indicate where such errors happen. The sliding time window can also be used to obtain a time sequence of posterior parameter distribution (or of best-fit calibration parameters). The dynamic parameter posterior will be further used to investigate the potential error source. Based on Bayes rule we can also observe how influential a parameter may be for model improvement.<br><br>We will show a corresponding visualization tool to indicate time periods where the model is potentially missing a process. We provide guidance to use showing how to use the dynamic parameter posterior to obtain insights on the error source and potentially to improve the model performance. The soil moisture model (HYDRUS 1D) was applied for a pilot test to prove the feasibility of this framework.</p>


Geophysics ◽  
1973 ◽  
Vol 38 (2) ◽  
pp. 349-358 ◽  
Author(s):  
Peter H. McGrath ◽  
Peter J. Hood

The magnetic anomalies caused by such diverse model shapes as the finite strike length thick dike, the vertical prism, thes loping step, the parallelepiped body, etc., may be obtained through an appropriate numerical integration of the expression for the magnetic effect produced by a finite thin plate. Using models generated in this manner, an automatic computer method has been developed at the Geological Survey of Canada for the interpretation of magnetic data. Because the magnetic anomalies produced by the various model shapes are nonlinear in parameters of shape and position, it is necessary to use an iterative procedure to obtain the values for the various model parameters which yield a least‐squares best‐fit anomaly curve to a set of discrete observed data. The interpretation method described in this paper uses the Powell algorithm for this purpose. The procedure can sometimes be made more efficient using a Marquardt modification to the Powell algorithm. Examples of the use of the method are presented for an elongated anomaly in the Moose River basin of the Hudson Bay lowlands in northern Ontario, and for an areally large elliptical anomaly in the Sverdrup basin of the Canadian Arctic Islands.


Author(s):  
I. A. Kuznetsov ◽  
A. V. Kuznetsov

In this paper, we first develop a model of axonal transport of tubulin-associated unit (tau) protein. We determine the minimum number of parameters necessary to reproduce published experimental results, reducing the number of parameters from 18 in the full model to eight in the simplified model. We then address the following questions: Is it possible to estimate parameter values for this model using the very limited amount of published experimental data? Furthermore, is it possible to estimate confidence intervals for the determined parameters? The idea that is explored in this paper is based on using bootstrapping. Model parameters were estimated by minimizing the objective function that simulates the discrepancy between the model predictions and experimental data. Residuals were then identified by calculating the differences between the experimental data and model predictions. New, surrogate ‘experimental’ data were generated by randomly resampling residuals. By finding sets of best-fit parameters for a large number of surrogate data the histograms for the model parameters were produced. These histograms were then used to estimate confidence intervals for the model parameters, by using the percentile bootstrap. Once the model was calibrated, we applied it to analysing some features of tau transport that are not accessible to current experimental techniques.


2015 ◽  
Vol 30 (31) ◽  
pp. 1550151 ◽  
Author(s):  
Prabir Rudra ◽  
Chayan Ranjit ◽  
Sujata Kundu

In this work, Friedmann–Robertson–Walker (FRW) universe filled with dark matter (DM) (perfect fluid with negligible pressure) along with dark energy (DE) in the background of Galileon gravity is considered. Four DE models with different equation of state (EoS) parametrizations have been employed namely, linear, Chevallier–Polarski–Lindler (CPL), Jassal–Bagla–Padmanabhan (JBP) and logarithmic parametrizations. From Stern, Stern+Baryonic Acoustic Oscillation (BAO) and Stern+BAO+Cosmic Microwave Background (CMB) joint data analysis, we have obtained the bounds of the arbitrary parameters [Formula: see text] and [Formula: see text] by minimizing the [Formula: see text] test. The best fit values and bounds of the parameters are obtained at 66%, 90% and 99% confidence levels which are shown by closed confidence contours in the figures. For the logarithmic model unbounded confidence contours are obtained and hence the model parameters could not be finitely constrained. The distance modulus [Formula: see text](z) against redshift [Formula: see text] has also been plotted for our predicted theoretical models for the best fit values of the parameters and compared with the observed Union2 data sample and SNe Type Ia 292 data and we have shown that our predicted theoretical models permits the observational datasets. From the data fitting it is seen that at lower redshifts [Formula: see text] the SNe Type Ia 292 data gives a better fit with our theoretical models compared to the Union2 data sample. So, from the data analysis, SNe Type Ia 292 data is the more favored data sample over its counterpart given the present choice of free parameters. From the study, it is also seen that the logarithmic parametrization model is less supported by the observational data. Finally, we have generated the plot for the deceleration parameter against the redshift parameter for all the theoretical models and compared the results with the work of Farooq et al., (2013).


2021 ◽  
Vol 9 ◽  
Author(s):  
Noelline Tsafack ◽  
Paulo A. V. Borges ◽  
Yingzhong Xie ◽  
Xinpu Wang ◽  
Simone Fattorini

Species abundance distributions (SADs) are increasingly used to investigate how species community structure changes in response to environmental variations. SAD models depict the relative abundance of species recorded in a community and express fundamental aspects of the community structure, namely patterns of commonness and rarity. However, the influence of differences in environmental conditions on SAD characteristics is still poorly understood. In this study we used SAD models of carabid beetles (Coleoptera: Carabidae) in three grassland ecosystems (desert, typical, and meadow steppes) in China. These ecosystems are characterized by different aridity conditions, thus offering an opportunity to investigate how SADs are influenced by differences in environmental conditions (mainly aridity and vegetation cover, and hence productivity). We used various SAD models, including the meta-community zero sum multinomial (mZSM), the lognormal (PLN) and Fisher’s logseries (LS), and uni- and multimodal gambin models. Analyses were done at the level of steppe type (coarse scale) and for different sectors within the same steppe (fine scale). We found that the mZSM model provided, in general, the best fit at both analysis scales. Model parameters were influenced by the scale of analysis. Moreover, the LS was the best fit in desert steppe SAD. If abundances are rarefied to the smallest sample, results are similar to those without rarefaction, but differences in models estimates become more evident. Gambin unimodal provided the best fit with the lowest α-value observed in desert steppe and higher values in typical and meadow steppes, with results which were strongly affected by the scale of analysis and the use of rarefaction. Our results indicate that all investigated communities are adequately modeled by two similar distributions, the mZSM and the LS, at both scales of analyses. This indicates (1) that all communities are characterized by a relatively small number of species, most of which are rare, and (2) that the meta-communities at the large scale maintain the basic SAD shape of the local communities. The gambin multimodal models produced exaggerated α-values, which indicates that they overfit simple communities. Overall, Fisher’s α, mZSM θ, and gambin α-values were substantially lower in the desert steppe and higher in the typical and meadow steppes, which implies a decreasing influence of environmental harshness (aridity) from the desert steppe to the typical and meadow steppes.


2020 ◽  
Vol 8 (12) ◽  
pp. 2009
Author(s):  
Caleb Wong de la Rosa ◽  
Kourtney A. Daniels ◽  
Rosana G. Moreira ◽  
Chris R. Kerth ◽  
Thomas M. Taylor

The U.S. rendering industry produces materials for use in further processed animal foods and feeds and is required to scientifically validate food safety hazard control. This study aimed to provide lethality validation for Salmonella enterica during simulated commercial rendering of whole chicken blood. Chicken blood was inoculated with a blend of multiple serovars of the pathogen (S. Heidelberg, Typhimurium, Senftenberg) and subjected to heating at 82.2, 87.8, or 93.3 °C; surviving cells were enumerated incrementally up to 5.0 min. Survivor data were modeled using the GInaFiT 1.7 freeware package. D-values and t7D (time to a 7.0 log10-cycle inactivation) values were generated from best-fit model parameters. Predictive modeling analysis revealed that the survival curves of Salmonella possessed log-linear components but also possessed shoulder and/or tail components. Mean D-values declined from 0.61 to 0.12 min as heating temperature was raised from 82.2 to 93.3 °F, respectively, differing by heating temperature (p = 0.023). t7D values differed significantly by heating temperature (p = 0.001), as was also the case for shoulder length (SL) (p = <0.0001), where, at lower temperatures, a shoulder was observed versus heating at 93.3 °F. These data aid scientific validation of Salmonella enterica inactivation during thermal rendering of poultry blood for use in further processed animal foods.


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