Abstract
Digital twin is one promising and key technology that emerged with Industry 4.0 to assist the decision-making process in multiple industries, enabling potential benefits such as reducing costs, and risk, improving efficiency, and supporting decision-making. Despite these, the decision-making approach of carrying out a what-if simulation study using digital twin models of each and every possible scenario independently is time-consuming and requires significant computational resources. The integration of recommendation systems within the digital twindriven decision-support framework can support the decision-making process by providing targeted scenario recommendations, reducing the decision-making time and imposing decision- making efficiency. However, recommendation systems have inherent challenges, such as cold-start, data sparsity, and prediction accuracy. The integration of trust and similarity measures with recommendation systems alleviates the challenges mentioned earlier, and the integration of machine learning techniques enables better recommendations through their ability to simulate human learning. Having this in mind, this paper proposes a trust-based recommendation approach using a reinforcement learning technique combined with similarity measures, which can be integrated within a digital twin-based what-if simulation decision-support system. This approach was experimentally validated by performing accurate recommendations in an industrial case study of a battery pack assembly line. The results show improvements in the proposed model regarding the accuracy of the prediction about the user rating of the recommended scenarios over the state-of-the-art recommendation approaches, particularly in coldstart and data sparsity scenarios.

Abstract
This work addresses the flexible job shop scheduling problem with transportation (FJSPT), which can be seen as an extension of both the flexible job shop scheduling problem (FJSP) and the job shop scheduling problem with transportation (JSPT). Regarding the former case, the FJSPT additionally considers that the jobs need to be transported to the machines on which they are processed on, while in the latter, the specific machine processing each operation also needs to be decided. The FJSPT is NP-hard since it extends NP-hard problems. Good-quality solutions are efficiently found by an operation-based multistart biased random key genetic algorithm (BRKGA) coupled with greedy heuristics to select the machine processing each operation and the vehicles transporting the jobs to operations. The proposed approach outperforms state-of-the-art solution approaches since it finds very good quality solutions in a short time. Such solutions are optimal for most problem instances. In addition, the approach is robust, which is a very important characteristic in practical applications. Finally, due to its modular structure, the multistart BRKGA can be easily adapted to solve other similar scheduling problems, as shown in the computational experiments reported in this paper.

Abstract
The demand for high-performance solutions for anomaly detection and forecasting fault events is increasing in the industrial area. The detection and forecasting faults from time-series data are one critical mission in the Internet of Things (IoT) data mining. The classical fault detection approaches based on physical modelling are limited to some measurable output variables. Accurate physical modelling of vehicle dynamics requires substantial prior information about the system. On the other hand, data-driven modelling techniques accurately represent the system's dynamic from data collection. Experimental results on large-scale data sets from Metro do Porto subsystems verify that our method performs high-quality fault detection and forecasting solutions. Also, health indicator obtained from the principal component analysis of the forecasting solution is applied to predict the remaining useful life.

Abstract
<p>Many clinical applications involve in-bed patient activity monitoring, from intensive care and neuro-critical infirmary, to semiology-based epileptic seizure diagnosis support or sleep monitoring at home, which require accurate recognition of in-bed movement actions from video streams.</p> <p>The major challenges of clinical application arise from the domain gap between common in-the-lab and clinical scenery (e.g. viewpoint, occlusions, out-of-domain actions), the requirement of minimally intrusive monitoring to already existing clinical practices (e.g. non-contact monitoring), and the significantly limited amount of labeled clinical action data available.</p> <p>Focusing on one of the most demanding in-bed clinical scenarios - semiology-based epileptic seizure classification – this review explores the challenges of video-based clinical in-bed monitoring, reviews video-based action recognition trends, monocular 3D MoCap, and semiology-based automated seizure classification approaches. Moreover, provides a guideline to take full advantage of transfer learning for in-bed action recognition for quantified, evidence-based clinical diagnosis support.</p> <p>The review suggests that an approach based on 3D MoCap and skeleton-based action recognition, strongly relying on transfer learning, could be advantageous for these clinical in-bed action recognition problems. However, these still face several challenges, such as spatio-temporal stability, occlusion handling, and robustness before realizing the full potential of this technology for routine clinical usage.</p>

Abstract
Technologies related to the transportation electrification have been gaining attention in recent years. One technology that stands out is wireless charging, which still presents numerous challenges in terms of design and optimization of parameters. This article proposes a design methodology for optimizing the performance of an inductive power transfer (IPT) for wireless charging of electric vehicles, taking into account operating limits. The proposed methodology uses a PSO (Particle Swarm Optimization) algorithm to find the design variables that maximize the eficiency. The methodology and the development of a 3.6 kW experimental setup are presented, resulting in a power transfer efficiency of 89.4 %. © 2023 IEEE.

Abstract
This paper describes the work developed to estimate the impact of the Special Regime Generation, SRG, in the generation cost in Portugal. Till the beginning of 2021 the values of the feed in tariffs paid to SRG were much larger than the market price paid to Normal Regime Generation, NRG, and this gap was often considered as a burden subsidized by consumers. In order to bring rational arguments to this discussion, several MSc Thesis were developed in recent years at the Engineering Faculty of Porto University to estimate the global generation cost in the country considering the current feed in regime and also admitting that generation paid feed in tariffs was reduced. This implied the calculation of the new market price if SRG was reduced and conversely NRG was increased. The results of the simulations developed for 2017, 2018, 2019 and 2020 indicate that the impact of SRG very much depends on the market price along the year. If the market price is reduced (for instance in good hydrological years as 2020) the elimination of SRG reduces the generation cost. Conversely, if the market price is high, the elimination of SRG tends to increase the generation cost.