Abstract

Abstract
Tropospheric ozone is a secondary pollutant having a negative impact on health and environment. To control and minimize such impact the European Community established regulations to promote a clean air all over Europe. However, when an episode is related with natural mechanisms as Stratosphere-Troposphere Exchanges (STE), the benefits of an action plan to minimize precursor emissions are inefficient. Therefore, this work aims to develop a tool to identify the sources of ozone episodes in order to minimize misclassification and thus avoid the implementation of inappropriate air quality plans. For this purpose, an artificial neural network model the Multilayer Perceptron - is used as a binary classifier of the source of an ozone episode. Long data series, between 2001 and 2010, considering the ozone precursors, Be-7 activity and meteorological conditions were used. With this model, 2-7% of a mean error was achieved, which is considered as a good generalization. Accuracy measures for imbalanced data are also discussed. The MCC values show a good performance of the model (0.65-0.92). Precision and F-1-measure indicate that the model specifies a little better the rare class. Thus, the results demonstrate that such a tool can be used to help authorities in the management of ozone, namely when its thresholds are exceeded due natural causes, as the above mentioned STE. Therefore, the resources used to implement an action plan to minimize ozone precursors could be better managed avoiding the implementation of inappropriate measures.

Abstract

Abstract
Increasing energy needs are accompanied by environmental responsibilities, since nowadays electricity companies operate in a competitive and sustainable energy framework. In this context, any proposal for action on energy efficiency becomes important for consumers to minimize operational costs. In electrical installations, electricity consumption can be decreased by reducing losses in the cables, associated with the overall efficiency of the equipment, allowing a better use of the installed power. The losses must be analysed in conjunction with all loads that contribute to the currents in the sections of an electrical installation. When replacing equipment in output distribution boxes with more efficient ones, the current in those sections is reduced in association with the decrease in power losses. This decrease, often forgotten, is taken into account in this work for the economic analysis of efficiency and sustainable electrical equipment. This paper presents a new software application that compares and chooses the best investment in the acquisition of electrical equipment. Simulation results obtained with the new software application are provided and are then validated with experimental results from a real electrical installation.

Abstract
For the first time in history, the world shows a clear trend towards aging. This poses an intrinsic hazard for the ever growing population, which becomes more vulnerable to common age-related illnesses and conditions. One of the most serious risks elders face is falling, as it is responsible for countless admissions to geriatric care institutions and thousands of deaths each year. In an effort to improve elders' safety and quality of life many groups have address the fall prevention issue, coming to several different results as of what variables are the most important to consider in a fall prediction tool. These variables range from qualitative aspects (history of falls, dementia, use of medication, etc.) to quantitative ones (total walked distance per day, walking cadence, center of mass, etc.), but none of them per se seems to deliver a definite and complete answer to the problem at hand. The paper herein aims to present a new hybrid approach, which combines both the highest co-related qualitative and quantitative biovariables in a single tool: the MATHOV + QAVS, which is proposed as a new fall assessment screening tool and eventually as baseline criteria for a complete elder fall prediction system. Copyright

Abstract