Risk assessment of atmospheric emissions using machine learning

Supervised and unsupervised machine learning algorithms are used to perform statistical and logical analysis of several transport and dispersion model runs which simulate emissions from a fixed source under different atmospheric conditions. <br><br> First, a clustering algorithm is used to automatically group the results of different transport and dispersion simulations according to specific cloud characteristics. Then, a symbolic classification algorithm is employed to find complex non-linear relationships between the meteorological input conditions and each cluster of clouds. The patterns discovered are provided in the form of probabilistic measures of contamination, thus suitable for result interpretation and dissemination. <br><br> The learned patterns can be used for quick assessment of the areas at risk and of the fate of potentially hazardous contaminants released in the atmosphere

Wavelet maxima curves of surface latent heat flux associated with two recent Greek earthquakes

International audienceMulti sensor data available through remote sensing satellites provide information about changes in the state of the oceans, land and atmosphere. Recent studies have shown anomalous changes in oceans, land, atmospheric and ionospheric parameters prior to earthquakes events. This paper introduces an innovative data mining technique to identify precursory signals associated with earthquakes. The proposed methodology is a multi strategy approach which employs one dimensional wavelet transformations to identify singularities in the data, and an analysis of the continuity of the wavelet maxima in time and space to identify the singularities associated with earthquakes. The proposed methodology has been employed using Surface Latent Heat Flux (SLHF) data to study the earthquakes which occurred on 14 August 2003 and on 1 March 2004 in Greece. A single prominent SLHF anomaly has been found about two weeks prior to each of the earthquakes

Post-test simulations for the NACIE-UP benchmark by STH codes

This paper illustrates the results obtained in the last phase of the NACIE-UP benchmark activity foreseen inside the EU SESAME Project. The purpose of this research activity, performed by system thermal–hydraulic (STH) codes, is finalized to the improvement, development and validation of existing STH codes for Heavy Liquid Metal (HLM) systems. All the participants improved their modelling of the NACIE-UP facility, respect to the initial blind simulation phase, adopting the actual experimental boundary conditions and reducing as much as possible sources of uncertainty in their numerical model. Four different STH codes were employed by the participants to the benchmark to model the NACIE-UP facility, namely: CATHARE for ENEA, ATHLET for GRS, RELAP5-3D© for the “Sapienza” University of Rome and RELAP5/Mod3.3(modified) for the University of Pisa. Three reference tests foreseen in the NACIE-UP benchmark and carried out at ENEA Brasimone Research Centre were analysed from four participants. The data from the post-test analyses, performed independently by the participant using different STH codes, were compared together and with the available experimental results and critically discussed

The effects of closeness on the election of a pairwise majority rule winner

Some studies have recently examined the effect of closeness on the probability of observing the monotonicity paradox in three-candidate elections under Scoring Elimination Rules. It has been shown that the frequency of such paradox significantly increases as elections become more closely contested. In this paper we consider the effect of closeness on one of the most studied notions in Social Choice Theory: The election of the Condorcet winner, i.e., the candidate who defeats any other opponent in pairwise majority comparisons, when she exists. To be more concrete, we use the well known concept of the Condorcet efficiency, that is, the conditional probability that a voting rule will elect the Condorcet winner, given that such a candidate exists. Our results, based on the Impartial Anonymous Culture (IAC) assumption, show that closeness has also a significant effect on the Condorcet efficiency of different voting rules in the class of Scoring and Scoring Elimination Rules

Mental fortitude training: An evidence-based approach to developing psychological resilience for sustained success

Drawing on the body of knowledge in this area, this article presents an evidence-based approach to developing psychological resilience for sustained success. To this end, the narrative is divided into three main sections. The first section describes the construct of psychological resilience and explains what it is. The second section outlines and discusses a mental fortitude training™ program for aspiring performers. The third section provides recommendations for practitioners implementing this program. It is hoped that this article will facilitate a holistic and systematic approach to developing resilience for sustained success

Air quality and urban sustainable development: the application of machine learning tools

[EN] Air quality has an efect on a population¿s quality of life. As a dimension of sustainable urban development, governments have been concerned about this indicator. This is refected in the references consulted that have demonstrated progress in forecasting pollution events to issue early warnings using conventional tools which, as a result of the new era of big data, are becoming obsolete. There are a limited number of studies with applications of machine learning tools to characterize and forecast behavior of the environmental, social and economic dimensions of sustainable development as they pertain to air quality. This article presents an analysis of studies that developed machine learning models to forecast sustainable development and air quality. Additionally, this paper sets out to present research that studied the relationship between air quality and urban sustainable development to identify the reliability and possible applications in diferent urban contexts of these machine learning tools. To that end, a systematic review was carried out, revealing that machine learning tools have been primarily used for clustering and classifying variables and indicators according to the problem analyzed, while tools such as artifcial neural networks and support vector machines are the most widely used to predict diferent types of events. The nonlinear nature and synergy of the dimensions of sustainable development are of great interest for the application of machine learning tools.Molina-Gómez, NI.; Díaz-Arévalo, JL.; López Jiménez, PA. (2021). Air quality and urban sustainable development: the application of machine learning tools. 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