Abstract
In this study, we analyze the potential of collaborative robotics in automated quality inspections in the industry. The development of a solution integrating an industrial vision system allowed evaluating the performance of collaborative robots in a real case. The use of these tools allows reducing quality defects as well as costs in the manufacturing process. In this system, image processing methods use resources based on depth and surface measurements with high precision. The system fully processes a panel, observing the state of the surface to detect any potential defect in the panels produced to increase the quality of production.

Abstract
The generation mix of Portugal now contains a significant amount of variable renewable energy sources (RES) and the amount of RES is expected to grow substantially. This has led to concerns being raised regarding the security of the supply of the Portuguese electric system as well as concerns relating to system inertia. Deploying and efficiently using various flexibility options is proposed as a solution to these concerns. Among these flexibility options proposed is the use of battery energy storage systems (BESSs) as well as relaxing system inertia constraints such as the system nonsynchronous penetration (SNSP). This article proposes a stochastic mixed-integer linear programming problem formulation, which examines the effects of deploying BESS in a power system. The model is deployed on a real-world test case and results show that the optimal use of BESS can reduce system costs by as much as 10% relative to a baseline scenario and the costs are reduced further when the SNSP constraint is relaxed. The amount of RES curtailment is also reduced with the increased flexibility of the power system through the use of BESS. Thus, the efficiency of the Portuguese transmission system is greatly increased by the use of flexibility measures, primarily the use of BESS.

Abstract
Optical trapping is a versatile and non-invasive technique for single particle manipulation. As such, it can be widely applied in the domains of particle identification and classification and thus used as a tool for monitoring physical and chemical processes. This creates an opportunity for integrating the method seamlessly into optofluidic chips, provided it can be automatized. Yet even though OT is well established in multiple scientific domains, a full stack approach to its integration into other technological devices is still lacking. This calls for solutions in tasks such as automatic trapping and signal analysis. In this manuscript, we describe the implementation of an algorithm seeking autonomous particle location and trapping. The methodology is based upon image-processing, allowing for particle location using real time image segmentation. A local thresholding algorithm is applied, followed by morphological techniques for closing shapes and excluding non-bounded regions - after which only the particles remain on the image. Once the centroid is identified, the stage is translated accordingly by piezo-electric actuators, followed by the laser activation. In this way, trapping is achieved, and one may proceed to analyze the forward scattered optical signal, after which a new particle inside the actuators range may be automatically trapped. This development, when compared with existent solutions involving holographic optical tweezers, allows for similar capabilities without using a spatial light modulator, thus dramatically reducing the setup costs of autonomous OT solutions. Therefore, when combined with particle classification techniques, this method is well suited for integration into possible optofluidic chips for autonomous sensing and monitoring of biochemical samples. © Published under licence by IOP Publishing Ltd.

Abstract
Nowadays, and increasingly, Artificial Intelligence (AI) occupies a leading role in the world, being used in the most diverse contexts. The retail sector is just one of them. The starting question that originated this article is: is Portugal receptive to the use of cutting-edge Artificial Intelligence in retail? In other words, what is the opinion of the Portuguese and residents in Portugal, as consumers, regarding the use of automation by retail companies? Based on the analysis of the answers to an online questionnaire (which obtained 132 answers), we will present our conclusions regarding this matter. The goal is to understand if Portuguese people / residents in Portugal are willing and interested in going to supermarkets like Amazon Go or Continente Labs (or Pingo Doce & GO NOVA). In addition, it is intended to understand the reasons that lead them to respond skeptically, so that, in the future, strategies may be initiated by the companies of the sector, which based on greater and better education, may clarify, and perhaps change their assumptions and convictions. The results of this study reveal that the Portuguese and residents in Portugal are not yet interested in using automated supermarkets, showing some mistrust and reticence towards this new technology, although they recognize that it can increase the speed and efficiency of the service. In fact, of the 37,1% of respondents that consider it quite attractive, about 84% believe it may have a significant impact on the reduction of time spent shopping.

Abstract
O processo de revisão sistemática de literatura em investigação continua a apresentar-se como um processo com um elevado custo de recursos humanos e de tempo. Com vista em otimizar este processo pretende-se estudar a performance da ferramenta de pesquisa Cognitive Search da Microsoft que contem funcionalidades de inteligência artificial (IA). Neste trabalho foi implementada uma solução de pesquisa, i.e., parametrização do serviço de pesquisa, que produz uma classificação de relevância dos artigos científicos. Uma análise qualitativa aos artigos científicos foi efetuada para analisar a performance da solução de pesquisa e habilidades de inteligência artificial da ferramenta. O tema da revisão sistemática é “how is artificial intelligence (AI) being used in Higher Education (HE) today, involving tree dimensions: learning with AI, learning about AI and learning for AI”.;The systematic review process of research literature continues to be a very time and human resource expensive process. With the objective of optimizing this process we intend to study the performance of Microsoft Cognitive Search service which contains artificial intelligence capabilities. In this work the search service tool was configured and parameterized (search solution) to produce a classification ranking of the research articles. These were manually analysed to infer on the performance of the search solution. The topic of the systematic review is “how is artificial intelligence (AI) being used in Higher Education (HE) today, involving tree dimensions: learning with AI, learning about AI and learning for AI”.

Abstract
Decarbonization of energy systems is one of the main tracks in the energy sector, and in this transition, green hydrogen assumes an important role. Considering the variability of renewable energy sources (RES), the flexibility of the hydrogen production could help dealing with imbalances. However, to truly contribute to a greener energy mix, a principle of additivity must be obeyed. In other words, to produce green hydrogen, the energy supplied to the electrolyzers must be renewable and must not entail a decrease in the RES consumed by other loads according to the energy strategic plans. This study integrates power flow tracing (PFT) technique within an optimal power flow (OPF) to determine and maximize the physical flow between the energy from RES generators and the electrolyzer through the existing grid. The proposed method was tested on both radial and meshed IEEE test grids. Simulation results showed that the electrolyzer green supply can be increased by controlling the dispatch of the distributed generators (e.g., CHP) according to the location of the electrolyzer. In addition, installing storage systems nearby load buses allows increasing the amount of green supply by using the RES-based electricity stored. © 2022 IEEE.