Abstract
There is a literature gap regarding the period representativeness bias associated with sample selection in longitudinal bibliometric studies. The purpose of this paper is to analyse and compare, in terms of period representativeness, the common methods used for selecting a sample of the highly impactful papers in a field/ journal. Using 92 593 papers (Information Science & Library Science area, 1977-2016), we compared, in terms of the number of papers/year, samples of the 100 most impactful papers, obtained with different selection options. We repeated the analysis also for Top500, Top2000, and Top20000. This study shows that the frequently used metrics to compare the impact of papers and to select a sample of spacing diaeresis most impactful papers p spacing diaeresis ublished in each year and each field may privilege specific periods while neglecting others. The main result of our study is that the percentile citation-based method reduces this y spacing diaeresis ear of publicationr spacing diaeresis epresentativeness bias. This paper draws attention to the importance of the sample selection, in bibliometric studies, and to the period representativeness bias associated with different choices to select the spacing diaeresis most impactful papers. spacing diaeresis

Abstract

Abstract
São diversos os benefícios da utilização de indicadores e métricas de sustentabilidade organizacional,e referem-se à possível antecipação de condições e tendências, fornecimento de avisos de possíveis ocorrências e situações que evitem danos aos aspectos econômico, social e ambiental e auxílio nos processos de gestão. O objetivo deste estudo foi analisar as métricas de sustentabilidade organizacional, identificadas na literatura acadêmica nos últimos cinco anos. Seguindo a metodologia PRISMA, foram relacionadas sete pesquisas relevantes e enquadradas nos critérios de seleção. As principais contribuições, por meio da revisão sistemática, apontaram para estudos compostos de diferentes aspectos (empresas, cidades, regiões) ligados à sustentabilidade. Foram diversas métricas identificadas, algumas focando mais aspectos sociais da população e ou funcionários e outras mais os aspectos ambientais e seus impactos na sociedade e nas organizações. As limitações do estudo são decorrentes do método escolhido que considerou os últimos cinco anos na base Scopus e artigos completos em inglês.

Abstract
As AI algorithms thrive on data, SCADA would be considered a natural ground for Artificial Intelligence (AI) applications to be developed, translating that avalanche of information into meaningful and fast insights to human operators. However, presently, the high complexity of the events, the data semantics, the large variety of equipment and technologies translate into very few AI applications developed in SCADA. Aware of the enormous potential yet to be explored, E-REDES partnered with INESC TEC to experiment on the development of two novel AI applications based on SCADA data. The first tool, called Alarm2Insights, identifies anomalous behaviours regarding the performance of the protection functions associated with HV and MV line panels. The second tool, called EventProfiler, uses unsupervised learning to identify similar events (i.e., with similar log messages) in HV line panels, and supervised learning to classify new events into previously defined clusters and detect unique or rare events. Aspects associated to data handling and pre-processing are also discussed. The project's results show a very promising potential of applying AI to SCADA data, enhancing the role of the operator and support him in doing better and more informed decisions. © 2021 The Institution of Engineering and Technology.

Abstract
Precise machine learning models for the early identification of anomalies based on biosignal data retrieved from bedside monitors could improve intensive care, by helping clinicians make decisions in advance and produce on-time responses. However, traditional models show limitations when dealing with the high complexity of this task. Layered Learning (LL) emerges as a solution, as it consists of the hierarchical decomposition of the problem into simpler tasks. This paper explores the uncovered potential of LL in the early detection of Acute Hypotensive Episodes (AHEs). We leverage information from the MIMIC-III Database to test different subdivisions of the main task and study how to combine the outcomes from distinct layers. In addition to this, we also test a novel approach to reduce false positives in AHE predictions.

Abstract
Imbalanced learning is one of the most relevant problems in machine learning. However, it faces two crucial challenges. First, the amount of methods proposed to deal with such problem has grown immensely, making the validation of a large set of methods impractical. Second, it requires specialised knowledge, hindering its use by those without such level of experience. In this paper, we propose the Automated Imbalanced Classification method, ATOMIC. Such a method is the first automated machine learning approach for imbalanced classification tasks. It provides a ranking of solutions most likely to ensure an optimal approximation to a new domain, drastically reducing associated computational complexity and energy consumption. We carry this out by anticipating the loss of a large set of predictive solutions in new imbalanced learning tasks. We compare the predictive performance of ATOMIC against state-of-the-art methods using 101 imbalanced data sets. Results demonstrate that the proposed method provides a relevant approach to imbalanced learning while reducing learning and testing efforts of candidate solutions by approximately 95%.