Abstract
Purpose This study aims to address the topic of ethics, responsibility and sustainability (ERS) orientation of students enrolled in schools of economics and management master's degrees. It examines the effect of educational background and gender on Portuguese students' orientation towards ERS, as well as the extent to which there is a relation between the scientific area of the master degree in which the student is enrolled and his/her ERS orientation. Design/methodology/approach The authors used a sample of 201 students from several master degrees offered by the School of Economics and Management of a large public Portuguese university and analysed their ERS orientation using a survey by questionnaire. Findings Findings suggest that there are differences in orientation across gender, with female students valuing ERS more than their male counterparts. Educational background has minimal effects on the responses. It was also found some sort of selection effect in terms of the scientific area of the master degree and ERS orientation. Originality/value This study contributes to the literature by analysing the issue of whether students with an educational background in economics and management present different ERS orientation than their counterparts, as well as by examining whether there is some sort of self-selection into the study of disciplines in which ERS orientation is likely to be a week. As far as the authors are aware, this is the first study analysing this type of issue regarding ERS.

Abstract
In a recent paper (10.1007/s11192-020-03386-9) we proposed a model to estimate the citations of an article in a database (Scopus/Web of Science) in which it is not indexed using the percentile rank of the database (Web of Science/Scopus) in which it is indexed. In this study we supplement the previous work with three advances: (1) by using 15 different research fields, corresponding to over 1 million papers, since we previously used only four fields; (2) by measuring the agreement between the percentile ranks in both databases using Lin's concordance correlation coefficient, since this coefficient has not been used previously to measure this agreement, but as a test with a sample of 15,400 papers to compare the actual and estimated number of citations; and (3) by using a robust data cleaning procedure. The results revealed a substantial concordance between percentile ranks of papers indexed in these two databases in all the research fields studied, and that this concordance is even stronger for high percentile values. This level of concordance suggests that we can consider the percentile of a paper in a database in which it is not indexed as being equal to the percentile of this paper in a database in which it is indexed. In other words, we increased the reliability of our previous conclusions that the percentile rank can be used as a citation database-normalization. The results of this study contribute to improve the use of citation counts in bibliometric studies, and to calculate research indicators when we need to use both bibliographic databases.

Abstract

Abstract
Performance estimation aims at estimating the loss that a predictive model will incur on unseen data. This process is a fundamental stage in any machine learning project. In this paper we study the application of these methods to time series forecasting tasks. For independent and identically distributed data the most common approach is cross-validation. However, the dependency among observations in time series raises some caveats about the most appropriate way to estimate performance in this type of data. Currently, there is no consensual approach. We contribute to the literature by presenting an extensive empirical study which compares different performance estimation methods for time series forecasting tasks. These methods include variants of cross-validation, out-of-sample (holdout), and prequential approaches. Two case studies are analysed: One with 174 real-world time series and another with three synthetic time series. Results show noticeable differences in the performance estimation methods in the two scenarios. In particular, empirical experiments suggest that blocked cross-validation can be applied to stationary time series. However, when the time series are non-stationary, the most accurate estimates are produced by out-of-sample methods, particularly the holdout approach repeated in multiple testing periods.

Abstract
Concept drift detection is a crucial task in data stream evolving environments. Most of the state of the art approaches designed to tackle this problem monitor the loss of predictive models. Accordingly, an alarm is launched when the loss increases significantly, which triggers some adaptation mechanism (e.g. retrain the model). However, this modus operandi falls short in many real-world scenarios, where the true labels are not readily available to compute the loss. These often take up to several weeks to be available. In this context, there is increasing attention to approaches that perform concept drift detection in an unsupervised manner, i.e., without access to the true labels. We propose a novel approach to unsupervised concept drift detection, which is based on a student-teacher learning paradigm. Essentially, we create an auxiliary model (student) to mimic the behaviour of the main model (teacher). At run-time, our approach is to use the teacher for predicting new instances and monitoring the mimicking loss of the student for concept drift detection. In a set of controlled experiments, we discovered that the proposed approach detects concept drift effectively. Relative to the gold standard, in which the labels are immediately available after prediction, our approach is more conservative: it signals less false alarms, but it requires more time to detect changes. We also show the competitiveness of our approach relative to other unsupervised methods. © 2020, Springer Nature Switzerland AG.

Abstract
Patients with end-stage renal disease should be educated and trained to take care of their own arteriovenous fistula (AVF) with the purpose of developing self-care behaviors concerning vascular access. This was a prospective and observational study. We designed this research to identify clinically meaningful self-care behavior profiles in hemodialysis (HD) patients, and it was carried out in a private dialysis unit in the Lisbon region, Portugal, involving 101 patients. The proportion of male patients was 66.3%, the mean age was 60.9 years, and the frequency of self-care behaviors was 71%. Cluster analysis based on the subscale scores grouped patients in two clusters named "moderate self-care" and "high self-care." Those profiles exhibit significant differences concerning gender, education, employment, dialysis vintage, AVF duration, and information on care with the AVF. Identification of self-care-behavior profiles in HD patients with AVF enables one to adjust education programs to the patients' characteristics.