Annoyance and welfare costs from the presence of renewable energy power plants: an application of the contingent valuation method

Sustainability is frequently defined by its three pillars: economically viable, socially equitable, and environmentally bearable. Consequently the evaluation of the sustainability of any decision, public or private, requires information on these three dimensions. This paper focuses on social sustainability. In the context of renewable energy sources, the examination of social sustainability requires the analysis of not only the efficiency but also the equity of its welfare impacts. The present paper proposes and applies a methodology to generate the information necessary to do a proper welfare analysis of the social sustainability of renewable energy production facilities. This information is key both for an equity and an efficiency analysis. The analysis focuses on the case of investments in renewable energy electricity production facilities, where the impacts on local residents’ welfare are often significantly different than the welfare effects on the general population. We apply the contingent valuation method to selected facilities across the different renewable energy power plants located in Portugal and conclude that local residents acknowledge differently the damage sustained by the type, location and operation of the plants. The results from these case studies attest to the need of acknowledging and quantifying the negative impacts on local communities when assessing the economic viability, social equity and environmental impact of renewable energy projects.

Using data mining for prediction of hospital length of stay: an application of the CRISP-DM Methodology

Hospitals are nowadays collecting vast amounts of data related with patient records. All this data hold valuable knowledge that can be used to improve hospital decision making. Data mining techniques aim precisely at the extraction of useful knowledge from raw data. This work describes an implementation of a medical data mining project approach based on the CRISP-DM methodology. Recent real-world data, from 2000 to 2013, were collected from a Portuguese hospital and related with inpatient hospitalization. The goal was to predict generic hospital Length Of Stay based on indicators that are commonly available at the hospitalization process (e.g., gender, age, episode type, medical specialty). At the data preparation stage, the data were cleaned and variables were selected and transformed, leading to 14 inputs. Next, at the modeling stage, a regression approach was adopted, where six learning methods were compared: Average Prediction, Multiple Regression, Decision Tree, Artificial Neural Network ensemble, Support Vector Machine and Random Forest. The best learning model was obtained by the Random Forest method, which presents a high quality coefficient of determination value (0.81). This model was then opened by using a sensitivity analysis procedure that revealed three influential input attributes: the hospital episode type, the physical service where the patient is hospitalized and the associated medical specialty. Such extracted knowledge confirmed that the obtained predictive model is credible and with potential value for supporting decisions of hospital managers.

Pervasive business intelligence platform to improve the quality of decision process in primary and secondary education – A Portuguese case study

Business Intelligence (BI) can be seen as a method that gathers information and data from information systems in order to help companies to be more accurate in their decision-making process. Traditionally BI systems were associated with the use of Data Warehouses (DW). The prime purpose of DW is to serve as a repository that stores all the relevant information required for making the correct decision. The necessity to integrate streaming data became crucial with the need to improve the efficiency and effectiveness of the decision process. In primary and secondary education, there is a lack of BI solutions. Due to the schools reality the main purpose of this study is to provide a Pervasive BI solution able to monitoring the schools and student data anywhere and anytime in real-time as well as disseminating the information through ubiquitous devices. The first task consisted in gathering data regarding the different choices made by the student since his enrolment in a certain school year until the end of it. Thereafter a dimensional model was developed in order to be possible building a BI platform. This paper presents the dimensional model, a set of pre-defined indicators, the Pervasive Business Intelligence characteristics and the prototype designed. The main contribution of this study was to offer to the schools a tool that could help them to make accurate decisions in real-time. Data dissemination was achieved through a localized application that can be accessed anywhere and anytime.