Abstract
A versatile, miniaturized, cost-effective, low-power wave profile and tide monitoring system, capable of long-term and scalable deployment, was developed to integrate pressure and temperature sensors in an RS485 network, for standalone operation with organized memory or real-time shared data monitoring. The pressure and temperature sensors are controlled by low-power microcontrollers, that communicate the data periodically to a datalogger, that depending on the application, store it in a removable SD card or send it to a server via Wi-Fi. The data is then analyzed to compensate for the loss in amplitude sensitivity according to the sensor's depth. The wave profile can be sampled at a maximum rate of 100 Hz, with a 1 cm resolution. The system was tested successfully in real-life conditions, in rivers Douro and Cavado, and off the coast of Viana do Castelo.

Abstract
Postoperative residual neuromuscular block (PRNB) is still a problem during the surgery procedures resulting in health problems, such as, airway obstruction, hypoxia and pulmonary aspiration. To perform more accurate monitoring of the patient during surgery quantitative neuromuscular blockade monitoring measuring TOF ratio has been recommended by medical institutions. There are some devices available using different techniques, however there are only a few number of clinicians using them, since those devices are costly and have difficult clinical set-up. This paper presents a systematic review of current devices for quantitative neuromuscular monitoring during the surgery procedure following the PRISMA methodology. This study was carried out to list the currently available devices and report the capabilities that are missing in these devices since 2017. The databases used to do the research were PubMed, Cochrane Library, PubMed Central (PMC), Web of Science, IEEE Xplore, ScienceDirect, Directory of Open Access Journals (DOAJ). 17 articles were selected, presenting comparisons between two devices using different techniques. Quantitative monitoring provides the most accurate TOF ratio measurement but still needs to be incentivized.

Abstract
The transition to Industry 4.0 provoked a transformation of industrial manufacturing with a significant leap in automation and intelligent systems. This paradigm shift has brought about a mindset that emphasizes predictive maintenance: detecting future failures when current behaviour of industrial processes and machines is thought to be normal. The constant monitoring of industrial equipment produces massive quantities of data that enables the application of machine learning approaches to this task. This study uses deep learning-based models to build a data-driven predictive maintenance framework for the air production unit (APU), a crucial system for the proper functioning of a Metro do Porto train. This public transport system moves thousands of people every day and train failures lead to delays and loss of trust by clients. Therefore, it is essential not only to detect APU failures before they occur to minimize negative impacts, but also to provide explanations for the failure warnings that can aid in decision-making processes. We propose an autoencoder architecture trained with an adversarial loss, known as the Wasserstein Autoencoder with Generative Adversarial Network (WAE-GAN), designed to detect sensor failures in systems connected to the APU. Our model can detect APU failures up to two hours before they occur, allowing timely intervention of the maintenance teams. We further augment our model with an explainability layer, by providing explanations generated by a rule-based model that focuses on rare events. Results show that our model is able to detect APU failures without any false alarms, fulfilling the requisites of Metro do Porto for early detection of the failures.

Abstract
Interest in integrating lot-sizing and cutting stock problems has been increasing over the years. This integrated problem has been applied in many industries, such as paper, textile and furniture. Yet, there are only a few studies that acknowledge the importance of uncertainty to optimise these integrated decisions. This work aims to address this gap by incorporating demand uncertainty through stochastic programming and robust optimisation approaches. Both robust and stochastic models were specifically conceived to be solved by a column generation method. In addition, both models are embedded in a rolling-horizon procedure in order to incorporate dynamic reaction to demand realisation and adapt the models to a multistage stochastic setting. Computational experiments are proposed to test the efficiency of the column generation method and include a Monte Carlo simulation to assess both stochastic programming and robust optimisation for the integrated problem. Results suggest that acknowledging uncertainty can cut costs by up to 39.7%, while maintaining or reducing variability at the same time.

Abstract
Bin picking is a challenging problem that involves using a robotic manipulator to remove, one-by-one, a set of objects randomly stacked in a container. When the objects are prone to entanglement, having an estimation of their pose and shape is highly valuable for more reliable grasp and motion planning. This paper focuses on modeling entangled tubes with varying degrees of curvature. An unconventional machine learning technique, Inductive Logic Programming (ILP), is used to construct sets of rules (theories) capable of modeling multiple tubes when given the cylinders that constitute them. Datasets of entangled tubes are created via simulation in Gazebo. Experiments using Aleph and SWI-Prolog illustrate how ILP can build explainable theories with a high performance, using a relatively small dataset and low amount of time for training. Therefore, this work serves as a proof-of-concept that ILP is a valuable method to acquire knowledge and validate heuristics for pose and shape estimation in complex bin picking scenarios.

Abstract
The objective of this work was to support the sustainable deployment of immersive learning environments, which face varied obstacles, including the lack of support infrastructures for active learning pedagogies. Sustainability from the perspective of the integration of these environments in educational practice entails situational awareness, workload, and the informed assessment ability of participants, which must be supported for such activities to be employed in a widespread manner. We have approached this wicked problem using the Design Science Research paradigm and produced the Inven!RA software architecture. This novel result constitutes a solution for developing software platforms to enable the sustainable deployment of immersive learning environments. The Inven!RA architecture is presented alongside four demonstration scenarios employed in its evaluation, providing a means for the situational awareness of immersive learning activities in support of pedagogic decision making.