A log-normal distribution model of the effect of bacteria and ear fenestration on hearing loss: a Bayesian approach

2004 ◽  
Vol 23 (3) ◽  
pp. 493-508 ◽  
Author(s):  
Byron J. Gajewski ◽  
Jack D. Sedwick ◽  
Patrick J. Antonelli
Keyword(s):  
Hearing Loss ◽  
Normal Distribution ◽  
Bayesian Approach ◽  
Distribution Model ◽  
Log Normal ◽  
Log Normal Distribution ◽  
Normal Distribution Model

A Log-Normal Distribution Model for the Molecular Weight of Aquatic Fulvic Acids

2000 ◽  
Vol 34 (6) ◽  
pp. 1103-1109 ◽  
Author(s):  
Stephen E. Cabaniss ◽  
Qunhui Zhou ◽  
Patricia A. Maurice ◽  
Yu-Ping Chin ◽  
George R. Aiken
Keyword(s):  
Molecular Weight ◽  
Normal Distribution ◽  
Fulvic Acids ◽  
Distribution Model ◽  
Log Normal ◽  
Log Normal Distribution ◽  
Normal Distribution Model

Comprehensive Source Identification and Apportionment Analysis of five Heavy Metals in Soils in Wenzhou City, China

2020 ◽  
Author(s):  
Shuai Shao ◽  
Bifeng Hu ◽  
Yin Zhou ◽  
Zhou Shi
Keyword(s):  
Heavy Metals ◽  
Normal Distribution ◽  
Source Identification ◽  
Mixture Distribution ◽  
Anthropogenic Sources ◽  
Distribution Model ◽  
Auxiliary Variables ◽  
Industrial Emissions ◽  
Log Normal ◽  
Log Normal Distribution

<p>Source identification and apportionment of heavy metals (HMs) has been a vital issue of soil contamination restoration. In this study, qualitive approaches (Finite mixture distribution model (FMDM) and raster based principal components analysis (RB-PCA)) as well as quantitative approach (positive matrix factorization (PMF)) were composed to identify and apportion sources of five HMs (Cd, Hg, As, Pb, Cr) with the help of several crucial auxiliary variables in Wenzhou City, China. The result of FMDM showed Cd, and Pb fitted for single log-normal distribution, while Hg fitted for double log-normal mixed distribution, and As, Cr presented triple log-normal distribution. Each element was identified and separated from natural or anthropogenic sources. An improved score interpolation map of PCA attached with corresponded auxiliary variables analysis suggested three main contribution sources including parental materials, mines exploiting and industrial emissions contributes most in the whole study area. Each element was further discussed for quantitative contributions through PMF model. Parental materials contributed to all elements (Cd, Hg, As, Pb, Cr) as 89.22%, 84.81%, 7.31%, 35.84%, 27.42%. Industrial emissions had a contribution as 2.94%, 80.77%, 15.93%, 4.79%, 25.63% for each element respectively. While Mine exploiting mixed with fertilizers inputs has dedicated for such five HMs as 7.84%,11.92%, 48.23%, 10.40% and 46.95%. Such results could efficiently be devoted to scientific decisions and strategies making regarding HMs pollution regulation in soils.</p>

scholarly journals Estimation on Reliability Models of Bearing Failure Data

2018 ◽  
Vol 2018 ◽  
pp. 1-21 ◽  
Author(s):  
Xia Xintao ◽  
Chang Zhen ◽  
Zhang Lijun ◽  
Yang Xiaowei
Keyword(s):  
Probability Distribution ◽  
Weibull Distribution ◽  
Normal Distribution ◽  
Distribution Model ◽  
Bearing Failure ◽  
Analysis Model ◽  
Failure Data ◽  
Log Normal ◽  
Log Normal Distribution ◽  
Two Parameter

The failure data of bearing products is random and discrete and shows evident uncertainty. Is it accurate and reliable to use Weibull distribution to represent the failure model of product? The Weibull distribution, log-normal distribution, and an improved maximum entropy probability distribution were compared and analyzed to find an optimum and precise reliability analysis model. By utilizing computer simulation technology and k-s hypothesis testing, the feasibility of three models was verified, and the reliability of different models obtained via practical bearing failure data was compared and analyzed. The research indicates that the reliability model of two-parameter Weibull distribution does not apply to all situations, and sometimes, two-parameter log-normal distribution model is more precise and feasible; compared to three-parameter log-normal distribution model, the three-parameter Weibull distribution manifests better accuracy but still does not apply to all cases, while the novel proposed model of improved maximum entropy probability distribution fits not only all kinds of known distributions but also poor information issues with unknown probability distribution, prior information, or trends, so it is an ideal reliability analysis model with least error at present.

Characterization of hetero-block copolymers by the log-normal distribution model

2016 ◽  
Vol 7 (17) ◽  
pp. 2992-3002 ◽  
Author(s):  
Michael J. Monteiro ◽  
Mikhail Gavrilov
Keyword(s):  
Molecular Weight ◽  
Chemical Composition ◽  
Block Copolymers ◽  
Normal Distribution ◽  
Distribution Model ◽  
Molecular Weight Distributions ◽  
Weight Distributions ◽  
Log Normal ◽  
Log Normal Distribution

Fitting multiple and of different chemical composition molecular weight distributions using the log-normal distribution (LND) model.

scholarly journals Using a combined power law and log-normal distribution model to simulate particle formation and growth in a mobile aerosol chamber

2016 ◽  
Author(s):  
M. Olin ◽  
T. Anttila ◽  
M. Dal Maso
Keyword(s):  
Normal Distribution ◽  
Power Law ◽  
Particle Formation ◽  
Distribution Model ◽  
Aerosol Formation ◽  
Aerosol Chamber ◽  
Relative Errors ◽  
Log Normal ◽  
Log Normal Distribution ◽  
Particle Formation And Growth

Abstract. We present the combined power law and log-normal distribution (PL+LN) model, a computationally efficient model to be used in simulations where the particle size distribution cannot be accurately represented by log-normal distributions, such as in simulations involving the initial steps of aerosol formation, where new particle formation and growth occur simultaneously, or in the case of inverse modelling. The model was validated against highly accurate sectional models using input parameter values that reflect conditions typical to particle formation occurring in the atmosphere and in vehicle exhaust, and tested in the simulation of a particle formation event performed in a mobile aerosol chamber at Mäkelänkatu street canyon measurement site in Helsinki, Finland. The number, surface area, and mass concentrations in the chamber simulation were conserved with the relative errors lower than 2 % using the PL+LN model, whereas a moment-based log-normal model and sectional models with the same computing time as with the PL+LN model caused relative errors up to 10 % and 135 %, respectively.

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