Abstract
The increasing integration of Distribution Energy Resources (DER) in the distribution system has brought the necessity of a change in grid management and also for better coordination between the Transmission System Operator (TSO) and the Distribution System Operator (DSO). This work proposes a reactive power management model to be used by DSOs, in which reactive power flexibility from DER, and also from On-Load Tap Changer (OLTC) transformers and capacitor banks are used to handle voltage problems that may arise in both transmission and distribution grids due to the uncertain production of Renewable Energy Sources (RES). Besides, it is proposed that the DSO may provide a service to the TSO, in which the latter requests a reactive power setpoint from the first one, in the TSO-DSO boundary. Adaptive robust optimization on an Alternating Current Optimal Power Flow (AC-OPF) is modelled, ensuring that the DSO receives a feasible solution and is able to manage congestion and voltage problems. The proposed model is compared with its stochastic equivalent to assess its strengths and drawbacks. To test and validate the proposed models, a 37-bus Medium Voltage (MV) distribution grid with high RES penetration is used. An important conclusion is that, though the robust model presents a safer solution than the stochastic model, the operator must be aware of the trade-off between the desired level of robustness and the expected operating cost.

Abstract
Multi-Label Stream Classification (MLSC) is the classification streaming examples into multiple classes simultaneously. Since new classes may emerge during the streaming process (concept evolution) and known classes may change over time (concept drift) it is challenging task. In real situations, concept drift and concept evolution occur in scenarios where the actual labels of arriving examples are never available; hence it is impractical to update decision models in a supervised fashion. This is known as Extreme Verification Latency, a topic that has not been well investigated in MLSC literature. This paper proposes a new method called MultI-label learNing Algorithm for Data Streams with Binary Relevance transformation (MINAS-BR), integrated with a Novelty Detection (ND) procedure for detecting concept evolution and concept drift, updating the model in an unsupervised fashion. Furthermore, since the label space is not static, we propose a new evaluation methodology for MLSC under extreme verification latency. Experiments over synthetic and real-world data sets with different concept drift and concept evolution scenarios confirmed the strategies employed in the MINAS-BR and presented relevant advances for handling streaming multi-label data.

Abstract
Simulators have been increasingly used on development and tests on several areas. They allow to speed up the development without damage and no extra costs. On realistic simulators, where kinematics play an important role, the modelling process should be imported for each component to be accurately simulated. Some robots are not yet modelled, as for example the Monera. This paper presents a model of a small vibration robot (Monera) that is acquired in a developed test-bed. A localisation ground truth is used to acquire the position of the Monera with actuating it. Linear and angular speeds acquired from real experiments allow to validate the proposed methodology.

Abstract
Optimizing research on the developmental origins of health and disease (DOHaD) involves implementing initiatives maximizing the use of the available cohort study data; achieving sufficient statistical power to support subgroup analysis; and using participant data presenting adequate follow-up and exposure heterogeneity. It also involves being able to undertake comparison, cross-validation, or replication across data sets. To answer these requirements, cohort study data need to be findable, accessible, interoperable, and reusable (FAIR), and more particularly, it often needs to be harmonized. Harmonization is required to achieve or improve comparability of the putatively equivalent measures collected by different studies on different individuals. Although the characteristics of the research initiatives generating and using harmonized data vary extensively, all are confronted by similar issues. Having to collate, understand, process, host, and co-analyze data from individual cohort studies is particularly challenging. The scientific success and timely management of projects can be facilitated by an ensemble of factors. The current document provides an overview of the 'life course' of research projects requiring harmonization of existing data and highlights key elements to be considered from the inception to the end of the project.

Abstract
This study aimed to confirm a factor structure of the Portuguese version of the 9-item Resilience Scale proposed by Siu et al. (2009). Participants were 121 university students, 79 females and 42 males, attending a Portuguese higher education institution, aged between 19 and 31 years (M=20.96; SD=1.96). The results showed that the one-factor model presented good adjustment indicators (& chi;2/df=1.554, CFI=.961, NNFI=.941, GFI=.996, RMSEA=.068, SRMR=.051, AIC=79.004 e MECVI=.690). As for reliability, Cronbach's alpha was .835, and composite reliability .766, both considered acceptable. We conclude that the Portuguese version of the Resilience Scale (ER-9) presents a unifactorial structure pointing to psychometric indicators that justify its application in samples of university students.

Abstract
The delivery of concrete is a crucial process in construction projects, and any delay or error can cause significant setbacks and added costs. Thus, effective real-time management of concrete delivery is essential to ensure timely and successful project completion. In this paper, we will discuss a practical and manufacturer-agnostic approach to real-time management of concrete delivery for construction named BET 4.0 that is being conceived with a close partnership with a construction company. This application provides the possibility to optimize the whole concreting process as it establishes the connection between all the relevant components and stakeholders involved in the construction process, namely the concrete plant, the transport, and the construction site, interfacing with all actors involved, and benefiting from real-time data produced by installed sensors in the several components such as machines, plants, or construction elements.