IMPROVED CLASSIFICATION MODELS TO DISTINGUISH NATURAL FROM ANTHROPIC OIL SLICKS IN THE GULF OF MEXICO: SEASONALITY AND RADARSAT-2 BEAM MODE EFFECTS UNDER A MACHINE LEARNING APPROACH

Distinguishing between natural and anthropic oil slicks is a challenging task, especially in the Gulf of Mexico, where these events can be simultaneously observed and recognized as seeps or spills.In this study, a powerful data analysis provided by machine learning (ML) methods was employed to develop, test, and implement a classification model (CM

Project Portfolio Selection (PPS) is a relevant activity in organizations to define the best set of projects to pursue their strategic objectives.Previous works have presented diverse and sometimes conflicting understandings of the Instant Mix meaning and implications of interdependencies to PPS.This paper presents a literature review encompassing

We investigate the impacts of Comprehensive Economic and Trade Agreement (CETA) liberalizations of trade and investment barriers on processed food markets.Using a four-region monopolistic competition model with heterogeneous food-processing firms that incorporates domestically operating, exporting, and multinational Brooch enterprise (MNE) firms, w

During the summer a thermal low-pressure system is locked over the Iberian Peninsula.We present a first analysis of such a system using the potential vorticity approach.Our results show that its main characteristic is the existence of a negative potential vorticity (PV) dome and a funnel-like structure Chest of Drawers for potential temperature, bo

An integrated network of environmental monitors was developed to continuously measure several airborne hazards in a manufacturing facility.The monitors integrated low-cost sensors to measure particulate matter, carbon monoxide, ozone and nitrogen dioxide, noise, temperature and humidity.The monitors were developed and tested in situ for three month