One of the major consequences of the digital revolution has been the increase in the use of electronic devices in health services. Despite their remarkable advantages, though, the use of computers and other visual display terminals for a prolonged time may have negative effects on vision, leading to a greater risk of Computer Vision Syndrome (CVS) among their users. In this study, the importance of ocular and visual symptoms related to CVS was evaluated, and the factors associated with CVS were studied, with the help of an algorithm based on regression trees and genetic algorithms. The performance of this proposed model was also tested to check its ability to predict how prone a worker is to suffering from CVS. The findings of the present research confirm a high prevalence of CVS in healthcare workers, and associate CVS with a longer duration of occupation and higher daily computer usage.

Due to the health impacts caused by exposures to air pollutants in urban areas, monitoring and forecasting of air quality parameters have become popular as an important topic in atmospheric and environmental research today. The knowledge on the dynamics and complexity of air pollutants behaviour has made artificial intelligence and statistical learning models as a useful tool for a more accurate pollutant concentration prediction. The aim of this study is to build a regression model of daily air quality prediction by using support vector machine (SVM) technique in Gijón urban area (Northern Spain) at local scale. To accomplish the objective, the observed data of nitrogen oxides, carbon monoxide, sulphur dioxide, ozone and dust for years 2006 to 2008 are used to create a highly nonlinear model of the air quality in the city based on SVM techniques. One objective of this model was to make a preliminary estimate of the dependence between primary and secondary pollutants in Gijón urban area at local scale. A second aim was to determine the factors with the greatest bearing on air quality in order to propose health and lifestyle improvements. This support vector regression model captures the main insight of statistical learning theory to obtain a good prediction of the dependence among the main pollutants in Gijón urban area since it shows a good agreement between the observed and predicted values of pollutants using statistical estimators as correlation coefficients, mean errors and root mean squared errors. Finally, conclusions of this study are exposed.

Adaptive optics reconstructors are needed to remove the effects of atmospheric distortion in optical systems of large telescopes. The use of reconstructors based on neural networks has been proved successful in recent times. Some of their properties require a specific characterization. A procedure, based in time series clustering algorithms, is presented to characterize the relationship between temporal structure of inputs and outputs, through analyzing the data provided by the system. This procedure is used to compare the performance of a reconstructor based in Artificial Neural Networks, with one that shows promising results, but is still in development, in order to corroborate its suitability previously to its implementation in real applications. Also, this procedure could be applied with other physical systems that also have evolution in time.

Mining companies are responsible for the impacts that result from their mining activities even after the mining period has ended. At the same time, at the European and international levels, there is a lack of a detailed operational methodology comprising environmental risks during and after closure of underground coal mines. The environmental risk aspects that need to be considered when planning the closure of an underground coal mine and post closure in the broader environmental context are the following: modification of water flow scheme, surface instability, mine gas emission on the surface, and water and soil pollution. In this study, we focus on assessing groundwater risk in the context of an underground coal mine closure and evaluating the selected risk mitigation strategies in terms of performance and cost. The results from this study could be used for developing a final closure groundwater assessment plan by selecting the most feasible treatment alternatives for different environmental impacts, together with the transitional monitoring that could guarantee a hazard level in compliance with land reuse and the use of natural resources. Finally, the cost-efficient monitoring and treatment programs are used to estimate the financial provisions needed to mitigate groundwater risks during underground coal mine closure contexts.