Abstract
This paper develops a benchmarking framework to support performance-based sunshine regulation in the water sector. It uses benefit-of-the-doubt composite indicators formulated with a directional distance function. Weight restrictions are incorporated in the model to account for different perspectives in the performance assessment. The framework is tested using data of the Portuguese regulation authority concerning the activity of wastewater operators. The information obtained using this framework reflects overall performance at the firm level and complements traditional evaluations of regulatory authorities based on the analysis of individual indicators. The results can be used to disseminate best practices, motivate continuous improvement, and foster enhancements in the governance of regulated utilities.

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The Information and Communication Technologies (ICT) plays a major role in tourism, travel and hospitality industry. The Integration of ICT in the tourism industry is essential for success of tourism enterprise, as such it is necessary to integrate ICT in higher education curricula. This paper analyzes the relevance given by the various Portuguese and Spanish institutions of higher education to ICT in their degrees. The analysis carried out was done in degree courses operating in the academic year 2018/2019, in Portuguese and Spanish universities and polytechnics. A comparison was also made with the reality of 2012/2013.

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Machine Learning is a field in which significant steps forward have been taken in the last years, resulting in a wide variety of available algorithms, for many different problems. Nonetheless, most of these algorithms focus on the training of static models, in the sense that the model stops evolving after the training phase. This is increasingly becoming a limitation, especially in an era in which datasets are increasingly larger and may even arrive as sequential streams of data. Frequently retraining a model, in these scenarios, is not realistic. In this paper we propose evoRF: a combination of a Random Forest with an evolutionary approach. Its key innovative aspect is the evolution of the weights of the Random Forest over time, as new data arrives, thus making the forest's voting scheme adapt to the new data. Older trees can also be replaced by newly trained ones, according to their accuracy, ensuring that the ensemble remains up to date without requiring a whole retraining.

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