Abstract
In the power system decarbonization roadmap, novel grid management tools and market mechanisms are fundamental to solving technical problems concerning renewable energy forecast uncertainty. This work proposes a predictive algorithm for procurement of grid flexibility by the system operator (SO), which combines the SO flexible assets with active and reactive power short-term flexibility markets. The goal is to reduce the cognitive load of the human operator when analyzing multiple flexibility options and trajectories for the forecasted load/RES and create a human-in-the-loop approach for balancing risk, stakes, and cost. This work also formulates the decision problem into several steps where the operator must decide to book flexibility now or wait for the next forecast update (time-to-decide method), considering that flexibility (availability) price may increase with a lower notification time. Numerical results obtained for a public MV grid (Oberrhein) show that the time-to-decide method improves up to 22% a performance indicator related to a cost-loss matrix, compared to the option of booking the flexibility now at a lower price and without waiting for a forecast update.

Abstract
Financial fraud is the cause of multi-billion dollar losses annually. Traditionally, fraud detection systems rely on rules due to their transparency and interpretability, key features in domains where decisions need to be explained. However, rule systems require significant input from domain experts to create and tune, an issue that rule induction algorithms attempt to mitigate by inferring rules directly from data. We explore the application of these algorithms to fraud detection, where rule systems are constrained to have a low false positive rate (FPR) or alert rate, by proposing RIFF, a rule induction algorithm that distills a low FPR rule set directly from decision trees. Our experiments show that the induced rules are often able to maintain or improve performance of the original models for low FPR tasks, while substantially reducing their complexity and outperforming rules hand-tuned by experts.

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Abstract
Fault tolerance (FT) is a critical aspect of industry, where systems are susceptible to disturbance and faults. Traditional FT models, based on the centralization of information to handle fault episodes, no longer meet the current industrial models based on Cyber-physical Systems (CPS). Self-healing is a promising approach for FT in CPS, consisting of the individual competence of each component in detect, diagnose and recover from failures. With this in mind, this paper discusses the engineering of self-healing fault-tolerance in industrial CPS, analyzing the maturation process of this paradigm from the local model through collaboration models and later to self-organization features. The paper also discusses the main research challenges that self-healing FT faces during this process.

Abstract
RESUMO A estreita codificação da área acadêmica das ciências da educação, na sua inscrição e relação com contextos de trabalho, no âmbito da formação de professores, torna problemática a possibilidade de exercício profissional noutros contextos, pese embora a transversalidade caraterística desses saberes. Procura-se neste artigo, com base em um estudo efetuado com diplomadas/os nessa área e já inseridas/os no mundo do trabalho há um período não inferior a sete anos, construir tipos ideais que caraterizem as relações que as suas trajetórias profissionais mantêm com a formação de base, procurando elucidar os percursos que os sujeitos são efetivamente capazes de construir. A análise dos dados permite-nos inferir que, para um número considerável das/os diplomadas/os entrevistados, a empregabilidade em educação, embora desafiante, verifica-se. São, no entanto, os modos e tempos distintos dessa profissionalização que conferem ao conceito de trajetória a pertinência da análise proposta.

Abstract