The Comribution and other three Parameter Distributions of Exponential Class

2020 ◽  
Vol 8 (1) ◽  
Author(s):  
Johnson Ademola Adewara ◽  
John Sunday Adeyeye
Keyword(s):  
Parameter Distributions

scholarly journals Topological vectors as a fingerprinting system for 2D-material flake distributions

2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Joyce C. C. Santos ◽  
Mariana C. Prado ◽  
Helane L. O. Morais ◽  
Samuel M. Sousa ◽  
Elisangela Silva-Pinto ◽  
...  
Keyword(s):  
Distribution Function ◽  
2D Materials ◽  
Shape Parameters ◽  
Representation System ◽  
Statistical Characterization ◽  
2D Material ◽  
Fast Pace ◽  
Parameter Distributions

AbstractThe production of 2D material flakes in large quantities is a rapidly evolving field and a cornerstone for their industrial applicability. Although flake production has advanced in a fast pace, its statistical characterization is somewhat slower, with few examples in the literature which may lack either modelling uniformity and/or physical equivalence to actual flake dimensions. The present work brings a methodology for 2D material flake characterization with a threefold target: (i) propose a set of morphological shape parameters that correctly map to actual and relevant flake dimensions; (ii) find a single distribution function that efficiently describes all these parameter distributions; and (iii) suggest a representation system—topological vectors—that uniquely characterizes the statistical flake morphology within a given distribution. The applicability of such methodology is illustrated via the analysis of tens of thousands flakes of graphene/graphite and talc, which were submitted to different production protocols. The richness of information unveiled by this universal methodology may help the development of necessary standardization procedures for the imminent 2D-materials industry.

scholarly journals Rainfall-induced release of microbes from manure: model development, parameter estimation, and uncertainty evaluation on small plots

2016 ◽  
Vol 14 (3) ◽  
pp. 443-459 ◽  
Author(s):  
Keewook Kim ◽  
Gene Whelan ◽  
Marirosa Molina ◽  
S. Thomas Purucker ◽  
Yakov Pachepsky ◽  
...  
Keyword(s):  
Model Development ◽  
Fecal Coliforms ◽  
Time Varying ◽  
Rainfall Runoff ◽  
Swine Slurry ◽  
Four Seasons ◽  
Release Curve ◽  
Parameter Values ◽  
Runoff Events ◽  
Parameter Distributions

A series of simulated rainfall-runoff experiments with applications of different manure types (cattle solid pats, poultry dry litter, swine slurry) was conducted across four seasons on a field containing 36 plots (0.75 × 2 m each), resulting in 144 rainfall-runoff events. Simulating time-varying release of Escherichia coli, enterococci, and fecal coliforms from manures applied at typical agronomic rates evaluated the efficacy of the Bradford–Schijven model modified by adding terms for release efficiency and transportation loss. Two complementary, parallel approaches were used to calibrate the model and estimate microbial release parameters. The first was a four-step sequential procedure using the inverse model PEST, which provides appropriate initial parameter values. The second utilized a PEST/bootstrap procedure to estimate average parameters across plots, manure age, and microbe, and to provide parameter distributions. The experiment determined that manure age, microbe, and season had no clear relationship to the release curve. Cattle solid pats released microbes at a different, slower rate than did poultry dry litter or swine slurry, which had very similar release patterns. These findings were consistent with other published results for both bench- and field-scale, suggesting the modified Bradford–Schijven model can be applied to microbial release from manure.

Conditioning Model Ensembles to Various Observed Data (Field and Regional Level) by Applying Machine-Learning-Augmented Workflows to a Mature Field with 70 Years of Production History

2021 ◽  
pp. 1-18
Author(s):  
Gisela Vanegas ◽  
John Nejedlik ◽  
Pascale Neff ◽  
Torsten Clemens
Keyword(s):  
Machine Learning ◽  
Oil Recovery ◽  
Numerical Models ◽  
Operating Conditions ◽  
Model Parameters ◽  
Large Set ◽  
Model Parameter ◽  
Production History ◽  
Hydrocarbon Fields ◽  
Parameter Distributions

Summary Forecasting production from hydrocarbon fields is challenging because of the large number of uncertain model parameters and the multitude of observed data that are measured. The large number of model parameters leads to uncertainty in the production forecast from hydrocarbon fields. Changing operating conditions [e.g., implementation of improved oil recovery or enhanced oil recovery (EOR)] results in model parameters becoming sensitive in the forecast that were not sensitive during the production history. Hence, simulation approaches need to be able to address uncertainty in model parameters as well as conditioning numerical models to a multitude of different observed data. Sampling from distributions of various geological and dynamic parameters allows for the generation of an ensemble of numerical models that could be falsified using principal-component analysis (PCA) for different observed data. If the numerical models are not falsified, machine-learning (ML) approaches can be used to generate a large set of parameter combinations that can be conditioned to the different observed data. The data conditioning is followed by a final step ensuring that parameter interactions are covered. The methodology was applied to a sandstone oil reservoir with more than 70 years of production history containing dozens of wells. The resulting ensemble of numerical models is conditioned to all observed data. Furthermore, the resulting posterior-model parameter distributions are only modified from the prior-model parameter distributions if the observed data are informative for the model parameters. Hence, changes in operating conditions can be forecast under uncertainty, which is essential if nonsensitive parameters in the history are sensitive in the forecast.

scholarly journals Regional parent flood frequency distributions in Europe – Part 2: Climate and scale controls

2014 ◽  
Vol 18 (11) ◽  
pp. 4391-4401 ◽  
Author(s):  
J. L. Salinas ◽  
A. Castellarin ◽  
S. Kohnová ◽  
T. R. Kjeldsen
Keyword(s):  
Flood Frequency ◽  
Mean Annual Precipitation ◽  
Annual Maximum ◽  
Catchment Scale ◽  
Frequency Distributions ◽  
Coefficient Of Skewness ◽  
Basin Area ◽  
Two Parameter ◽  
Parameter Distributions ◽  
Annual Maximum Series

Abstract. This study aims to better understand the effect of catchment scale and climate on the statistical properties of regional flood frequency distributions. A database of L-moment ratios of annual maximum series (AMS) of peak discharges from Austria, Italy and Slovakia, involving a total of 813 catchments with more than 25 yr of record length is presented, together with mean annual precipitation (MAP) and basin area as catchment descriptors surrogates of climate and scale controls. A purely data-based investigation performed on the database shows that the generalized extreme value (GEV) distribution provides a better representation of the averaged sample L-moment ratios compared to the other distributions considered, for catchments with medium to higher values of MAP independently of catchment area, while the three-parameter lognormal distribution is probably a more appropriate choice for drier (lower MAP) intermediate-sized catchments, which presented higher skewness values. Sample L-moment ratios do not follow systematically any of the theoretical two-parameter distributions. In particular, the averaged values of L-coefficient of skewness (L-Cs) are always larger than Gumbel's fixed L-Cs. The results presented in this paper contribute to the progress in defining a set of "process-driven" pan-European flood frequency distributions and to assess possible effects of environmental change on its properties.

scholarly journals Extended Czjzek model applied to NMR parameter distributions in sodium metaphosphate glass

2013 ◽  
Vol 25 (25) ◽  
pp. 255402 ◽  
Author(s):  
Filipe Vasconcelos ◽  
Sylvain Cristol ◽  
Jean-François Paul ◽  
Laurent Delevoye ◽  
Francesco Mauri ◽  
...  
Keyword(s):  
Sodium Metaphosphate ◽  
Parameter Distributions
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