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Current Issue [Vol. 05, No. 04] [April 2019]


Paper Title :: Prediction of Concentrations of Suspended Particle Levels of 2.5 micrometers (PM2.5) in Mexico City with Probability Distribution Functions and its Trend
Author Name :: M. Sc. Zenteno Jiménez José Roberto
Country :: Mexico
Page Number :: 01-17
The study includes a data analysis from 2010 to 2018, it was proposed to obtain the best or better probability distribution functions that Model PM2.5 concentrations in mexico city, using the following PDF, Gama Distribution Function, Extreme Value Distribution Function, Gumbel Distribution Function and Weibull Distribution Function, the maximum likelihood and moments method was used to obtain the estimators, and with the aid of the Matlab 2017 program, RMSE, MSE, were used to assess the forecast model, determination coefficient, prediction approximation and approximation index, in turn, an analysis is made to observe its trend within the period using the method of obtaining new functions of normal probability distribution and extreme value by bayesian inference to concentration data of daily highs later corroborating with the official air page of mexico city.
Keywords:2.5 Micrometer Particulate Material, Probability Distributions, Fit Indicators, Bayesian Inference.
[1]. A.J. Jakeman, J.A. Taylor, R.W. Simpson, Modeling distributions of air pollutant concentrations - II. Estimation of one and two parameters statistical distributions, Atmos. Environ., 20 (1986) 2435-2447.
[2]. Berger, A., Melice, J. L. and Demuth, C. L. (1982) Statistical distributions of daily and high atmospheric SO2 – concentrations. Atmospheric Environment. 16 (5), 2863 – 2877
[3]. Data base of PM2.5 website of México City http://www.aire.cdmx.gob.mx/
[4]. Georgopoulos, P.G. and Seinfeld, J.H. (1982) ‘Statistical distribution of air pollutant concentration’, Environmental Science Technology, Vol. 16, pp.401A–416A.
[5]. Gumbel, E.J., 1958. Statistics of Extremes. Columbia University Press, New York, p. 164.

Paper Title :: Prediction of Concentrations of Suspended Particle Levels of 10 micrometers (PM10) in México City with Probability Distribution Functions, Trend 2010-2018
Author Name :: M.Sc. Zenteno Jimenez José Roberto
Country :: México
Page Number :: 18-36
The study includes an analysis of data from 2010 to 2018, it was proposed to obtain the best or better features probability distribution model the concentrations of PM10 in México City using the following pdf, probability distribution function gama, probability density function of extreme value, probability distribution function gumbel and probability distribution function weibull, to obtain estimators by method maximum likelihood and moments was used and helped the Matlab 2017 program, assessment forecasting model RMSE, MSE, coefficient of determination and Index of Approximation, at the same time an analysis is made to observe its tendency within the period to data of concentrations of daily maximum after corroborating with the official page of air of méxico city, the trend analysis is done with Bayesian Inference.
Keywords: Particulate Matter of 10 micrometers, probability distributions, adjustment indicators, Extreme Value Theory, Bayesian Inference
[1]. AJ Jakeman, JA Taylor, RW Simpson, Modeling of air pollutant distributions Concentrations - II. Estimation of one and two parameters statistical distributions, Atmos. Environ., 20 (1986) 2435-2447.
[2]. Berger, A., Melice JL and Demuth, CL (1982) of distributions Statistical daily and high atmospheric SO2 - Concentrations. Atmospheric Environment. 16 (5), 2863-2877
[3]. PM10 data base of Mexico City website ofhttp://www.aire.cdmx.gob.mx/
[4]. Georgopoulos, PG and Seinfeld, JH (1982) 'Statistical distribution of air pollutant concentration', Environmental Science & Technology, Vol. 16, pp.401A-416A.
[5]. Gumbel, EJ, 1958. Statistics of Extremes. Columbia University Press, New York, p. 164.

Paper Title :: Advanced EU Situational Awareness System MSSAS: Tools and Functionalities
Author Name :: Friedrich Steinhäusler
Country :: Austria
Page Number :: 37-51
The Multi-stakeholder Standardized Situational Awareness System (MSSAS) is based on the situational awareness (SA) requirements of crisis managers, identified in two international surveys, four technical expert meetings, two public workshops and discussions with members of the Advisory Board and among consortium members. In addition, the results of the analysis of 23 EC-funded, pertinent projects with SA-relevance have been taken into account. MSSAS Concept consists of SA Reference Architecture, SA Functionalities and SA Tools. MSSAS integrates state-of-the art technologies, ranging from space-based observations to wearable detectors and computer-assisted decision support systems. Special MSSAS Modules have been developed for Law Enforcement, Firefighters, Paramedics and Technical Relief Organisations.
Keywords: Situational Awareness, Crisis Management,Technologies, Emergency Services
[1]. The Editors of Encyclopaedia Britannica, Indian Ocean tsunami of 2004, LAST UPDATED: 19 December 2018; https://www.britannica.com/event/Indian-Ocean-tsunami-of-2004 (last visited 15 February 2019)
[2]. Georges Guiochon and Laurent Jacob, On the Catastrophic Explosion of the AZF plant in Toulouse (September 21, 2001), AIChE Spring Meeting and Global Congress on Process Safety, 4 April 2012; https://www.aiche.org/academy/videos/conference-presentations/on-catastrophic-explosion-azf-plant-toulouse-september-21-2001
[3]. F. Javier Barroso, Thirteen years after deadly Madrid train bombings, memorial in disrepair, El Pais, 13 March 2017; https://elpais.com/elpais/2017/03/13/inenglish/1489396094_806567.html
[4]. Melissa Eddy, A Year After the Berlin Market Attack, Germany Admits Mistakes, New York Times, 19 December 2017;https://www.nytimes.com/2017/12/19/world/europe/berlin-attack-memorial.html
[5]. Kelsey Davenport, WMD Terrorism, Arms Control Association, January/February 2019;https://www.armscontrol.org/taxonomy/term/27









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