Apparent water losses can be problematic to water companies’ revenues. This type of loss is very difficult to detect and quantify and is often associated with water meter anomalies. This study was motivated by a water company’s challenge that links a decrease in water consumption to water meters’ malfunction. The aim is to develop a strategy to detect decreasing water usage patterns, contributing to meter performance assessment. The basis of the approach is a combination of statistical methods. First, the time series of billed water consumption is decomposed using Seasonal-Trend decomposition based on Loess. Next, breakpoint analysis is performed on the seasonally adjusted time series. After that, the Mann–Kendall test and Sen’s slope estimator are used to analyze periods of progressive decrease changes in water consumption, defined by breakpoints. A quantitative indicator of this change is proposed. The strategy was successfully applied to eight-time series of water consumption from the Algarve, Portugal.

Qualifying for European competitions allows football clubs to have access to the two most important football competitions played in Europe, UEFA Champions League and UEFA Europa League. Thereby, participation in these important competitions has both direct (participation prizes) and indirect (player transactions, television rights, box office, advertising, sponsorship) impacts on the sustainability of European football clubs’ accounts. The current competition model, which now includes more football teams, has become more attractive for small clubs. In this context, this research aims to analyze the impact of qualification for European competitions on the sustainability of a strong economic and financial structure of small clubs through a case study analysis of two small Portuguese clubs. The quantitative analysis is also supported by a comparative analysis, which was reinforced with nonparametric statistical analysis. The results show that the economic and financial impact on small clubs was not immediate, but was beneficial if it was managed efficiently. This research contributes to increasing knowledge of the decision-making agents of clubs that are participating for the first time in European competitions, or, if recurrent, are seeking answers and guidelines for the effective management of football teams.

Coronavirus pandemic has impacted the whole education system. Suddenly, presential classrooms are replaced by online learning, leaving students and teachers to deal with various problems without any specific preparation. This research study examines the perceptions of 78 students from a Portuguese Higher Education (HE) institution towards online learning during the Coronavirus disease 19 (COVID-19) pandemic. Two months after the implementation of the online learning, they responded to a survey whose responses were analyzed by using descriptive statistics and independent Chi-squared tests. The findings of this study reveal the difficulties that the students encountered, like the lack of familiar/spatial conditions at home, the failures of Internet connection, and the fear of clarifying doubts in this context. The preference for face-to-face classes was also expressed. However, the selected platform used for the online classes, the appropriate development of the explanations and the digital contents made available by the teachers were appreciated by the students. The present research also investigates the factors that affected the study motivation of the students and the future plans regarding their studies.

Entrepreneurship is a theme of global interest, and it is the subject of investigations conducted by many researchers and projects. In particular, the Global Entrepreneurship Monitor project is a global project that involves several countries and years of surveys on entrepreneurship indicators. This study focuses on the 12 indicators of the entrepreneurial ecosystem defined by the Entrepreneurial Framework Conditions (EFCs). The EFCs are specifically related to the quality of the entrepreneurial ecosystem. Using clustering techniques, the present study analyzes how European experts’ perceptions on the EFCs of their home country have changed between 2000 and 2019. The main finding is the existence of significant differences between the clusters obtained over the years and between countries. Therefore, in theoretical terms, this dynamical behavior in relation to the entrepreneurial conditions of economies should be considered in future works, namely, those concerning the definition of the number of clusters, which, according to the internal validation measures computed in this work, should be two.

In order to address one of the most challenging problems in hospital management - patients' absenteeism without prior notice - this study analyses the risk factors associated with this event. To this end, through real data from a hospital located in the North of Portugal, a prediction model previously validated in the literature is used to infer absenteeism risk factors, and an explainable model is proposed, based on a modified CART algorithm. The latter intends to generate a human-interpretable explanation for patient absenteeism, and its implementation is described in detail. Furthermore, given the significant impact, the COVID-19 pandemic had on hospital management, a comparison between patients' profiles upon absenteeism before and during the COVID-19 pandemic situation is performed. Results obtained differ between hospital specialities and time periods meaning that patient profiles on absenteeism change during pandemic periods and within specialities.

This study investigates the scheduling of mechanical repairs performed at a Portuguese firm in the automobile sector. The aim is to reduce the amount of time that vehicles spend inactive between interventions by developing a mathematical model that takes into account the available resources and mechanics, the necessary interventions, and the time required for each repair. To accomplish this, a mixed-integer linear programming (MILP) model was employed, incorporating various variables to schedule interventions, allocate resources, and determine start times for each vehicle. The problem was formulated using the AMPL modeling language, and real-world instances of the problem, derived from data provided by the company, were solved using the Gurobi solver. Results show that the developed model significantly improves the scheduling of the vehicles’ repairs at the firm, leading to a reduction of 67% on average in the downtime of the vehicles and allowing an automatic correct schedule of the mechanical interventions. Moreover, the comparison of the scheduling obtained from the developed model and the firm’s procedure shows that interventions on vehicles arriving at the repair shop are mostly repaired on the day of entry, allowing for quicker delivery to the customer.