Abstract
Ancillary services play a fundamental role in the operation of electricity systems. In the Iberian Peninsula, since mid-2014, ancillary services have gained a transnational dimension, namely through the introduction of cross border balancing replacement reserves between the Portuguese and the Spanish Transmission System Operators (TSOs). This paper evaluates the impact of replacement reserves on the Portuguese electricity system, from the onset of this mechanism until the end of 2017, as a new contribution to earlier studies. It also describes the pecuniary impact of tertiary transactions, the identification, and categorization of possible different scenarios of tertiary mobilization, and the respective impact on the internal tertiary mobilization. On the one hand, the Iberian electricity system is one of the most influenced by a high penetration of intermittent renewables, and therefore one of the best candidates to experience increased benefits from the platform. On the other hand, the Portuguese TSO is one of the most peripheral TSOs in Europe that benefits more from the market integration in various dimensions of the electricity sector.

Abstract
This work proposes a new approach for the segmentation of the extensor tendon in ultrasound images of the second metacarpophalangeal joint (MCPJ). The MCPJ is known to be frequently involved in early stages of rheumatic diseases like rheumatoid arthritis. The early detection and follow up of these diseases is important to start and adapt the treatments properly and, in that way, preventing irreversible damage of the joints. This work relies on an active contours framework, preceded by a phase symmetry preprocessing and with prior knowledge energies, to automatically identify the extensor tendon. Active contours methods are widely used in ultrasound images because of their robustness to speckle noise and ability to join unconnected smaller regions into a coherent shape. The tendon is formulated as a line so open ended active contours were used. Phase symmetry highlights the tendon, by setting a proper scale range and angle span. The distance between structures and the tendon slope were also included to enforce the model based on anatomical characteristics. And finally, the concavity measures were used because, given the anatomy of the finger, we know that the tendon line should have less than two concavities. To solve the active contours energy minimization a genetic algorithm approach was used. Several energy metric configurations were compared using the modified Hausdorff distance and results showed that this segmentation is not only possible, but exhibits errors smaller than 0.5 mm with a confidence of 95% with the phase symmetry preprocessing and energies based on the line neighborhood, area ratio, slope, and concavity measurements.

Abstract
The emergence of online social networks provided users with an easy way to publish and disseminate content, reaching broader audiences than previous platforms (such as blogs or personal websites) allowed. However, malicious users started to take advantage of these features to disseminate unreliable content through the network like false information, extremely biased opinions, or hate speech. Consequently, it becomes crucial to try to detect these users at an early stage to avoid the propagation of unreliable content in social networks’ ecosystems. In this work, we introduce a methodology to extract large corpus of unreliable posts using Twitter and two databases of unreliable websites (OpenSources and Media Bias Fact Check). In addition, we present an analysis of the content and users that publish and share several types of unreliable content. Finally, we develop supervised models to classify a twitter account according to its reliability. The experiments conducted using two different data sets show performance above 94% using Decision Trees as the learning algorithm. These experiments, although with some limitations, provide some encouraging results for future research on detecting unreliable accounts on social networks.

Abstract
The aim of this study was to evaluate the potential of Fourier transform infrared (FTIR) spectroscopy coupled with chemometric methods to detect fish adulteration. Muscles of Atlantic salmon (Salmo salar) (SS) and Salmon trout (Onconrhynchus mykiss) (OM) muscles were mixed in different percentages and transformed into mini-burgers. These were stored at 3 degrees C, then examined at 0, 72, 160, and 240 h for deteriorative microorganisms. Mini-burgers was submitted to Soxhlet extraction, following which lipid extracts were analyzed by FTIR. The principal component analysis (PCA) described the studied adulteration using four principal components with an explained variance of 95.60%. PCA showed that the absorbance in the spectral region from 721, 1097, 1370, 1464, 1655, 2805, to 2935, 3009 cm 1 may be attributed to biochemical fingerprints related to differences between SS and OM. The partial least squares regression (PLS-R) predicted the presence/absence of adulteration in fish samples of an external set with high accuracy. The proposed methods have the advantage of allowing quick measurements, despite the storage time of the adulterated fish. FTIR combined with chemometrics showed that a methodology to identify the adulteration of SS with OM can be established, even when stored for different periods of time.

Abstract
Technological evolutions lead to changes in production processes; the Fourth Industrial Revolution has been called Industry 4.0, as it integrates Cyber-Physical Systems and the Internet of Things into supply chains. Large complex networks are the core structure of Industry 4.0: any node in a network can demand a task, which can be answered by one node or a set of them, collaboratively, when they are connected. In this paper, the aim is to verify how (i) network's connectivity (average degree) and (ii) the number of levels covered in nodes search impacts the total of production tasks completely performed in the network. To achieve the goal of this paper, two hypotheses were formulated and tested in a computer simulation environment developed based on a modeling and simulation study. Results showed that the higher the network's average degree is (their nodes are more connected), the greater are the number of tasks performed; in addition, generally, the greater are the levels defined in the search for nodes, the more tasks are completely executed. This paper's main limitations are related to the simulation process, which led to a simplification of production process. The results found can be applied in several Industry 4.0 networks, such as additive manufacturing and collaborative networks, and this paper is original due to the use of simulation to test this kind of hypotheses in an Industry 4.0 production network.

Abstract
This paper proposes a logistic planner with supplementary 3D spatial representations to enhance and interact with traditional logistic systems on the context of mobile manipulators performing internal logistics operations. By defining a hierarchical structure, the logistic world model, as the central entity synchronized between multiple system components, the reliability and accuracy of the logistic system is strengthened. The proposed approach aims at implementing a robust and intuitive solution for the set-up of mobile manipulator based logistic systems. The logistic planner includes a web based interface for fast setup of the warehouse layout based on robot sensing, as well as the definition of missions for the fleet of robotic systems.