Fractional modeling of blood ethanol concentration system with real data application

2019 ◽  
Vol 29 (1) ◽  
pp. 013143 ◽  
Author(s):  
Sania Qureshi ◽  
Abdullahi Yusuf ◽  
Asif Ali Shaikh ◽  
Mustafa Inc ◽  
Dumitru Baleanu
Keyword(s):  
Ethanol Concentration ◽  
Real Data ◽  
Blood Ethanol Concentration ◽  
Data Application ◽  
Fractional Modeling

scholarly journals Effect of acute postexercise ethanol intoxication on the neuroendocrine response to resistance exercise

2000 ◽  
Vol 88 (1) ◽  
pp. 165-172 ◽  
Author(s):  
L. Perry Koziris ◽  
William J. Kraemer ◽  
Scott E. Gordon ◽  
Thomas Incledon ◽  
Howard G. Knuttgen
Keyword(s):  
Resistance Exercise ◽  
Ethanol Concentration ◽  
Pooled Analysis ◽  
Cortisol Concentration ◽  
Neuroendocrine System ◽  
Sympathoadrenal System ◽  
Ethanol Intoxication ◽  
Blood Ethanol Concentration ◽  
Neuroendocrine Response ◽  
Prolonged Effect

This investigation was conducted to determine the effect of postexercise ethanol intoxication (21.97 ± 1.09 mmol/l blood) on the response of selected aspects of the neuroendocrine system to a resistance exercise (Ex) session. Nine resistance-trained men (25.0 ± 1.4 yr, 179.4 ± 3.4 cm, 79.7 ± 3.3 kg) were used to compare three 3-day treatments: control, Ex, and ethanol after exercise (ExEt). Blood was collected serially from an antecubital vein before exercise, immediately after exercise, and for pooled analysis at 20–40 (2 samples), 60–120 (4 samples), and 140–300 (9 samples) min after exercise on day 1 and in the morning (2 samples each) on days 2 and 3. Ethanol did not increase circulating epinephrine, norepinephrine, or cortisol concentration (Cort) above Ex elevations. At 60–120 min, only ExEt Cort was greater than control Cort. Concentrations of testosterone, luteinizing hormone, and corticotropin were not affected by either treatment. It is concluded that, although this blood ethanol concentration is insufficient to acutely increase Cort above that caused by Ex alone, it appears that ethanol may have a prolonged effect beyond the Ex response. This blood ethanol concentration does not further stimulate the sympathoadrenal system during the postexercise response.

scholarly journals Slash Truncation Positive Normal Distribution and Its Estimation Based on the EM Algorithm

Symmetry ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 2164
Author(s):  
Héctor J. Gómez ◽  
Diego I. Gallardo ◽  
Karol I. Santoro
Keyword(s):  
Expectation Maximization Algorithm ◽  
Likelihood Estimation ◽  
Real Data ◽  
Simulation Studies ◽  
R Software ◽  
Data Application ◽  
The Em Algorithm ◽  
Kurtosis Parameter ◽  
Computational Implementation ◽  
Moments Method

In this paper, we present an extension of the truncated positive normal (TPN) distribution to model positive data with a high kurtosis. The new model is defined as the quotient between two random variables: the TPN distribution (numerator) and the power of a standard uniform distribution (denominator). The resulting model has greater kurtosis than the TPN distribution. We studied some properties of the distribution, such as moments, asymmetry, and kurtosis. Parameter estimation is based on the moments method, and maximum likelihood estimation uses the expectation-maximization algorithm. We performed some simulation studies to assess the recovery parameters and illustrate the model with a real data application related to body weight. The computational implementation of this work was included in the tpn package of the R software.

The impact of blood ethanol concentration on the classification of head injury severity in traumatic brain injury

Brain Injury ◽  
2015 ◽  
Vol 29 (13-14) ◽  
pp. 1648-1653 ◽  
Author(s):  
Pål Rønning ◽  
Per Ole Gunstad ◽  
Nils-Oddvar Skaga ◽  
Iver Arne Langmoen ◽  
Knut Stavem ◽  
...  
Keyword(s):  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Head Injury ◽  
Injury Severity ◽  
Ethanol Concentration ◽  
Blood Ethanol Concentration ◽  
The Impact ◽  
Head Injury Severity

scholarly journals Causal variance decompositions for institutional comparisons in healthcare

2019 ◽  
Vol 29 (7) ◽  
pp. 1972-1986
Author(s):  
Bo Chen ◽  
Keith A Lawson ◽  
Antonio Finelli ◽  
Olli Saarela
Keyword(s):  
Random Effect ◽  
Real Data ◽  
Hospital Performance ◽  
Case Mix ◽  
Link Functions ◽  
Variation In Care ◽  
Residual Variation ◽  
Data Application ◽  
Variance Decompositions ◽  
Random Effect Models

There is increasing interest in comparing institutions delivering healthcare in terms of disease-specific quality indicators (QIs) that capture processes or outcomes showing variations in the care provided. Such comparisons can be framed in terms of causal models, where adjusting for patient case-mix is analogous to controlling for confounding, and exposure is being treated in a given hospital, for instance. Our goal here is to help identify good QIs rather than comparing hospitals in terms of an already chosen QI, and so we focus on the presence and magnitude of overall variation in care between the hospitals rather than the pairwise differences between any two hospitals. We consider how the observed variation in care received at patient level can be decomposed into that causally explained by the hospital performance adjusting for the case-mix, the case-mix itself, and residual variation. For this purpose, we derive a three-way variance decomposition, with particular attention to its causal interpretation in terms of potential outcome variables. We propose model-based estimators for the decomposition, accommodating different link functions and either fixed or random effect models. We evaluate their performance in a simulation study and demonstrate their use in a real data application.

Model Diagnostics for Bayesian Networks

2006 ◽  
Vol 31 (1) ◽  
pp. 1-33 ◽  
Author(s):  
Sandip Sinharay
Keyword(s):  
Model Checking ◽  
Bayesian Networks ◽  
Real Data ◽  
Diagnostic Tools ◽  
Model Diagnostics ◽  
Posterior Predictive Model Checking ◽  
Item Fit ◽  
Item Functioning ◽  
Data Application ◽  
Educational Assessments

Bayesian networks are frequently used in educational assessments primarily for learning about students’ knowledge and skills. There is a lack of works on assessing fit of Bayesian networks. This article employs the posterior predictive model checking method, a popular Bayesian model checking tool, to assess fit of simple Bayesian networks. A number of aspects of model fit, those of usual interest to practitioners, are assessed using various diagnostic tools. This article suggests a direct data display for assessing overall fit, suggests several diagnostics for assessing item fit, suggests a graphical approach to examine if the model can explain the association among the items, and suggests a version of the Mantel–Haenszel statistic for assessing differential item functioning. Limited simulation studies and a real data application demonstrate the effectiveness of the suggested model diagnostics.

Diffraction images and their attributes based on asymmetric summation: real data application

2020 ◽  
Author(s):  
Maxim I. Protasov ◽  
◽  
Vladimir A. Tcheverda ◽  
Valery V. Shilikov ◽  
◽  
...  
Keyword(s):  
Seismic Data ◽  
Real Data ◽  
Data Sets ◽  
Data Application ◽  
Diffraction Imaging

The paper deals with a 3D diffraction imaging with the subsequent diffraction attribute calculation. The imaging is based on an asymmetric summation of seismic data and provides three diffraction attributes: structural diffraction attribute, point diffraction attribute, an azimuth of structural diffraction. These attributes provide differentiating fractured and cavernous objects and to determine the fractures orientations. Approbation of the approach was provided on several real data sets.

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