Abstract
Resumo O abandono escolar é um problema complexo que afeta a maioria dos programas de graduação pós-secundária, em todo o mundo. O curso de engenharia industrial do Instituto ISVOUGA, localizado em Santa Maria da Feira, Portugal, não é exceção. Este estudo usou um conjunto de dados contendo informações gerais dos estudantes e suas notas para as unidades curriculares já avaliadas. A partir deste conjunto de dados, foram selecionados dezessete preditores potenciais: cinco intrínsecos (gênero, estado civil, situação profissional, idade e regime de dedicação aos estudos – integral ou parcial) e doze extrínsecos (as notas em todas as doze unidades curriculares ministradas durante os dois primeiros semestres do curso). O objetivo principal desta investigação foi prever a probabilidade de um estudante abandonar o curso com base nos referidos preditores. Foi usada uma regressão logística binária para classificar os estudantes como tendo uma probabilidade alta ou baixa de não se reinscreverem no curso. Para validar se a metodologia utilizada é apropriada para o estudo em causa, a precisão obtida com o modelo de regressão logística foi comparada, por via de uma validação cruzada com cinco partições, com a precisão obtida pela utilização de três métodos muito utilizados em data mining: One R, K Nearest Neighbors e Naive Bayes. O modelo de regressão logística identificou quatro variáveis significativas na previsão do abandono escolar (as classificações nas unidades curriculares de ciência dos materiais, eletricidade, cálculo 1 e química). Os dois preditores mais influentes do abandono dos estudantes são não conseguir aprovação nas unidades curriculares menos exigentes: ciência dos materiais e eletricidade. Ao contrário do que seria de supor antes desta investigação, descobrimos que a não aprovação em unidades curriculares mais exigentes, como física ou estatística, não tem influência significativa no abandono escolar.

Abstract
Air temperature data from many locations worldwide are only available as series of daily minima and maxima temperatures. Historically, several different approaches have been used to estimate the actual daily mean temperature, as only in the last two or three decades automatic thermometers are able to compute its actual value. The most common approach is to estimate it by averaging the daily minima and maxima. When only daily minima and maxima are available, an alternative approach, proposed by Dall'Amico and Hornsteiner in 2006, uses the two daily extremes together with next day minima temperature and a coefficient related to the local daily astronomical sunset time. Additionally, the method uses two optimizable coefficients related to the region's temperature profile. In order to use this approach it is necessary to optimize the region's unknown parameters. For this optimization, it is necessary a dataset containing the maxima, minima, and the actual daily mean temperatures for at least one year. In this research, for the period 2007-2014, we used three datasets of minima, maxima and actual mean temperatures obtained at three automatic meteorological stations located in the Douro Valley to optimize the two unknown parameters in the Dall'Amico and Hornsteiner approach. Moreover, we compared the actual mean daily temperatures available from the three datasets with the correspondent values estimated by using i) the usual approach of averaging the daily maxima and minima temperatures and ii) the Dall'Amico and Hornsteiner approach. Results show that the former approach overestimates, on average, the daily mean temperatures by 0.5 degrees C. The Dall'Amico and Hornsteiner approach showed to be a better approximation of mean temperatures for the three meteorological stations used in this research, being unbiased relative to the actual mean values of daily temperatures. In conclusion, this research confirms that the Dall'Amico and Hornsteiner is a better approach to estimate the mean daily temperatures and provides the optimized parameters for three sites located at each of the three sub-regions of the Douro Valley (Baixo Corgo, Cima Corgo and Douro Superior).

Abstract
Service organizations increasingly understand the importance of managing the customer experience to enhance customer satisfaction and loyalty. This study aims to develop a better understanding of the customer experience by investigating how the customer’s internal mechanisms influence it. That is, how it is perceived and processed at three different levels (visceral, behavioral and reflective), which determines a person’s cognitive and emotional state. To this purpose an exploratory multi-method ethnographic study was undertaken in a healthcare service. The results showed the emotions provoked by the service experience at each level. These levels are interconnected and impact each other working together to influence a person’s cognitive and emotional state, and thus playing a critical role in the overall evaluation of a service. Results show that elements such as servicescape aesthetics, face-to-face and non-human interactions influence emotions and service evaluations. The service should be designed in a way that induces positive emotions, and a feeling of being in control. Especially in healthcare services there is a need to balance the conflicting responses of the emotional stages that may be triggered at the visceral and behavioral levels, while providing reassurance and calm at the reflective level that the health problem is going to be taken care. Using service design approaches this understanding of the customers’ brain can be translated into improving the customer experience. © 2018, Springer Nature Switzerland AG.

Abstract
This article is about making decisions concerning the management of sustainability, decisions that may influence the use or protection of natural resources or address difficult societal choices. Managers have more and more to tackle a diversity of problems in a rigorous and transparent way. One of the distinctive features of these decisions is that managers must give attention to both the values of people affected and factual information concerning the potential consequences of actions. This imposes the adoption of new methods for structuring spaces, strategy alternatives, and organizational planning. The support from operational research analysts becomes increasingly important, as we are dealing with people mostly without strong quantitative or model-building backgrounds. With the presence of different perspectives and mental models, behavior elements are at the core of the problem and unintentional biases in model use may occur. Our intention is to help promote the transference of knowledge to and within companies so that they may assure resilience. We found in general morphological analysis a great help for that. We want to make available a meta-model based on Operational Research for fields involving public resources and multiple interests to aid current and future managers of companies. We conclude the article with two case studies to illustrate our approach.

Abstract
Hospital Information System is the most fragile component of the health care system in the countries in development process. The modernization of the health care system does not integrate a rigid reflection on the installation of these tools into hospitals, which remain foreign with the strategies of these countries. The main objective of this research is to present a systematic review on the relationship between process approach and continuous improvement with the development and continuity of hospital information systems. Hospital Information System and its sustainability are key factors for the functioning of services in Hospital Institutions which requires principles respect of quality management as the process approach among others. The main question to be treated in this paper is " Which reasons have been given for the views that the process approach is or isn't helping to have and give a Hospital Information System in continuous development?", in order to contribute to the systematization of knowledge in this area, the main objective of this research is to present a systematic review on the relationship between process approach and continuous improvement with the development and continuity of hospital information systems. The systematic review methodology was the PRISMA Statement (R), the search granted to find 7735 based on defined key-words, and after a preliminary examination, according to the exclusion conditions and the eligibility criteria 20 papers were considered relevant to a more detailed study. After an analysis of all relevant documents we have tried to reveal the important gap, which we will try to explore more in future investigations.

Abstract
This study aims to evaluate the economic efficiency of Nursing Homes owned by 96 Santas Casas da Misericordia (SCM) and the determinants that influenced their efficiency in 2012 and 2013. The SCM are the oldest non-profit entities, which belong to Third Sector in Portugal, provide this social response and receive significant financial contributions annually from the state. The study is developed in two stages. In the first stage, the efficiency scores were calculated through the non-parametric DEA technique. In the second stage, Tobit regression is used to verify the effect of certain organizational variables on efficiency, namely the number of users and existence of Nursing Home chains. The results of the DEA model show that the efficiency average is 81.9%, and only 10 out of 96 Nursing Homes are efficient. Tobit regression shows that the number of users has a positive effect on the efficiency of Nursing Homes, whereas the existence of Nursing Home chains affects their efficiency negatively.