scholarly journals The Statistical Distributions of PM2.5 in Rayong and Chonburi Provinces, Thailand

2020 ◽  
Vol 8 (3) ◽  
Author(s):  
Vanida Pongsakchat ◽  
Pattaraporn Kidpholjaroen
Keyword(s):  
Fine Particulate Matter ◽  
Statistical Distribution ◽  
Distribution Model ◽  
Statistical Distributions ◽  
Average Data ◽  
Fine Particulate ◽  
Pearson Type ◽  
Anderson Darling ◽  
Log Normal ◽  
Log Normal Distribution

The fine particulate matter (PM2.5) concentrations is one of the most important issues that are often discussed since it has a greater impact on human health. Statistical distribution modeling plays an important role in predicting PM2.5 concentrations. This research aims to find the optimum statistical distribution model of PM2.5 in Rayong Province and Chonburi Province. The daily average data from 2014 – 2019 for Rayong and from 2015 – 2019 for Chonburi were using. Five statistical distributions were compared. A proper statistical distribution that represents PM2.5 concentrations has been chosen based on three criteria include Anderson-Darling statistic and RMSE. The results show that Pearson type VI distribution performs better compared to other distributions for PM2.5 concentrations in Rayong. For Chonburi, the proper statistical distribution is Log normal distribution.  

scholarly journals Extreme geomagnetic activities: a statistical study

2020 ◽  
Vol 72 (1) ◽  
Author(s):  
Ryuho Kataoka
Keyword(s):  
Magnetic Storms ◽  
Magnetic Observatory ◽  
Statistical Distributions ◽  
Limited Data ◽  
Data Set ◽  
Sudden Commencements ◽  
The One ◽  
Log Normal ◽  
Geomagnetic Activities ◽  
Log Normal Distribution

Abstract Statistical distributions are investigated for magnetic storms, sudden commencements (SCs), and substorms to identify the possible amplitude of the one in 100-year and 1000-year events from a limited data set of less than 100 years. The lists of magnetic storms and SCs are provided from Kakioka Magnetic Observatory, while the lists of substorms are obtained from SuperMAG. It is found that majorities of events essentially follow the log-normal distribution, as expected from the random output from a complex system. However, it is uncertain that large-amplitude events follow the same log-normal distributions, and rather follow the power-law distributions. Based on the statistical distributions, the probable amplitudes of the 100-year (1000-year) events can be estimated for magnetic storms, SCs, and substorms as approximately 750 nT (1100 nT), 230 nT (450 nT), and 5000 nT (6200 nT), respectively. The possible origin to cause the statistical distributions is also discussed, consulting the other space weather phenomena such as solar flares, coronal mass ejections, and solar energetic particles.

Method to Assist in the Scheduling of Add-on Surgical Cases-Upper Prediction Bounds for Surgical Case Durations Based on the Log-normal Distribution 

1998 ◽  
Vol 89 (5) ◽  
pp. 1228-1232 ◽  
Author(s):  
Jinshi Zhou ◽  
Franklin Dexter
Keyword(s):  
Normal Distribution ◽  
Operating Room ◽  
Statistical Distribution ◽  
Distribution Free ◽  
Surgical Case ◽  
Normal Method ◽  
Log Normal ◽  
Or Manager ◽  
Log Normal Distribution

Background A problem that operating room (OR) managers face in running an OR suite on the day of surgery is to identify "holes" in the OR schedule in which to assign "add-on" cases. This process necessitates knowing the typical and maximum amounts of time that the case is likely to require. The OR manager may know previous case durations for the particular surgeon performing a particular scheduled procedure. The "upper prediction bound" specifies with a certain probability that the duration of the surgeon's next case will be less than or equal to the bound. Methods Prediction bounds were calculated by using methods that (1) do not assume that case durations follow a specific statistical distribution or (2) assume that case durations follow a log-normal distribution. These bounds were tested using durations of 48,847 cases based on 15,574 combinations of scheduled surgeon and procedure. Results Despite having 3 yr of data, 80 or 90% prediction bounds would not be able to be calculated using the distribution-free method for 35 or 49% of future cases versus 22 or 22% for the log-normal method, respectively. Prediction bounds based on the log-normal distribution overestimated the desired value less often than did the distribution-free method. The chance that the duration of the next case would be less than or equal to its 90% bound based on the log-normal distribution was within 2% of the expected rate. Conclusions Prediction bounds classified by scheduled surgeon and procedure can be accurately calculated using a method that assumes that case durations follow a log-normal distribution.

scholarly journals Comparative Study of Statistical Distributions in Electromigration-Induced Failures of Al/Cu Thin-Film Interconnects

1994 ◽  
Vol 16 (2) ◽  
pp. 119-126 ◽  
Author(s):  
M. I. Loupis ◽  
J. N. Avaritsiotis ◽  
G. D. Tziallas
Keyword(s):  
Thin Film ◽  
Grain Size ◽  
Extreme Value Distribution ◽  
Statistical Distributions ◽  
Post Processing ◽  
Failure Times ◽  
Life Data ◽  
Cu Thin Film ◽  
Log Normal ◽  
Log Normal Distribution

In electromigration failure studies, it is in general assumed that electromigration-induced failures may be adequately modelled by a log-normal distribution. Further to this, it has been argued that a lognormal distribution of failure times is indicative of electromigration mechanisms. We have combined post processing of existing life-data from Al/Cu + TiW bilayer interconnects with our own results from Al/Cu interconnects to show that the Log Extreme Value distribution is an equally good statistical model for electromigration failures, even in cases where grain size exceeds the linewidth. The significance of such a modelling is particularly apparent in electromigration failure rate prediction.

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

scholarly journals THE NETWORK OF INTER-REGIONAL DIRECT INVESTMENT STOCKS ACROSS EUROPE

2007 ◽  
Vol 10 (01) ◽  
pp. 29-51 ◽  
Author(s):  
STEFANO BATTISTON ◽  
JOAO F. RODRIGUES ◽  
HAMZA ZEYTINOGLU
Keyword(s):  
Power Law ◽  
Direct Investment ◽  
Policy Making ◽  
Scaling Laws ◽  
Statistical Distributions ◽  
Regional Investment ◽  
Log Normal ◽  
Geographical Space ◽  
Log Normal Distribution ◽  
Different Levels

We present an analysis of inter-regional investment stocks within Europe from a complex networks perspective. We consider two different levels: first, we compute the inward–outward investment stocks at the level of firms, based on ownership shares and number of employees; then we estimate the inward–outward investment stock at the level of regions in Europe, by aggregating the ownership network of firms, based on their headquarter location. To our knowledge, there is no similar approach in the literature so far, and we believe that it may lead to important applications for policy making. In the present paper, we focus on the statistical distributions and the scaling laws, while in further studies we will analyze the structure of the network and its relation to geographical space. We find that while outward investment and activity of firms are power law distributed with a similar exponent, for regions these quantities are better described by a log-normal distribution. At both levels we also find scaling laws relating investment to activity and connectivity. In particular, we find that investment stock scales as a power law of the connectivity, as previously found for stock market data.

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>

Simulation of the supercooling process and frazil evolution in turbulent flows

2004 ◽  
Vol 31 (6) ◽  
pp. 915-926 ◽  
Author(s):  
Shi Qiang Ye ◽  
Jay Doering
Keyword(s):  
Turbulent Flows ◽  
Heat Balance ◽  
Laboratory Experiments ◽  
Distribution Model ◽  
Secondary Nucleation ◽  
Frazil Ice ◽  
Rotating Flume ◽  
Flow Turbulence ◽  
Log Normal ◽  
Log Normal Distribution

A model to simulate the supercooling process and frazil ice evolution in a counter-rotating flume is developed based on a series of laboratory experiments. The characteristics of the supercooling process were found to be related to air temperature and flow turbulence. Frazil ice growth was observed to follow a log-normal distribution model. The model avoids the need to simulate seeding, secondary nucleation, flocculation–breakup, and gravitational removal. Only the overall heat balance is considered during the entire process. The simulations show good agreements with experimental time–temperature curves and frazil evolution.Key words: supercooling, frazil ice, size distribution, concentration, turbulence, simulation.

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