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
Indoor localization systems are an important topic in the field of manufacturing process. A computational infrastructure based on Bluetooth low energy technology with state estimators for filtering is used to localize employees in the shop floor. The researchers' motivation is two-folds: implement an indoor tracking system while promoting manage production time. In this paper, we discuss the first prototype of a localization system adapted to address these goals. Experimental results show that the system for our case study, achieves a localization accuracy of less than three meters. © 2022 IEEE.

Abstract
Over the years, many techniques have been developed for the stabilisation of bone fractures. The study of the adhesion of bone-to-bone cement is an important step towards the development of new immobilization systems. Although bone cement has been used for more than fifty years, very few studies have been performed regarding the evaluation of fracture properties. In this work, numerical and experimental investigations were conducted to evaluate the strain energy release rate under mode I loading in a bone-cement bonded joint, using the Double Cantilever Beam (DCB) test. Cohesive zone laws were also measured combining the finite element method with non-linear elastic fracture mechanics. This has been made in a cortical bone bonded joint with polymethylmethacrylate (PMMA). Consistent results have been obtained regarding fracture toughness in a widely used bone-to-bone cement joint in many biomedical applications.

Abstract
Portugal will build the warm support and access structure (WSS) to the mid-infrared, first generation ELT instrument METIS. The particular characteristics of METIS and the ELT pose several challenges to designing the WSS according to requirements, as well challenges to the assembly and integration of the WSS. We here provide you an overview of those challenges, as well as strategies to overcome and mitigate issues related to the mass and dimensions of the WSS.

Abstract
Future power systems encourage the use of renewable energy resources, among them wind power is of great interest, but its power output is intermittent in nature which can affect the stability of the power system and increase the risk of blackouts. Therefore, a forecasting model of the wind speed is essential for the optimal operation of a power supply with an important share of wind energy conversion systems. In this paper, two wind speed forecasting models based on multiple meteorological measurements of wind speed and temperature are proposed and compared according to their mean squared error (MSE) value. The first model concerns the artificial intelligence based on neural network (ANN) where several network configurations are proposed to achieve the most suitable structure of the problem, while the other model concerned the Adaptive Neuro-Fuzzy Inference System (ANFIS). To enhance the results accuracy, the invalid input samples are filtered. According to the computational results of the two models, the ANFIS has delivered more accurate outputs characterized by a reduced mean squared error value compared to the ANN-based model.

Abstract
Precision fertilization implies the need to identify the variability of soil fertility, which is costly and time-consuming. Remotely measured data can be a solution. Using this strategy, a study was conducted, in a vineyard, to delineate different management zones using two indicators: apparent soil electrical conductivity (ECa) and normalized difference vegetation index (NDVI). To understand the contribution of each indicator, three scenarios were used for zone definition: (1) using only NDVI, (2) only ECa, or (3) using a combination of the two. Then the differences in soil fertility between these zones were assessed using simple statistical methods. The results indicate that the most beneficial strategy is the combined use of the two indicators, as it allowed the definition of three distinct zones regarding important soil variables and crop nutrients, such as soil total nitrogen, Mg2+ cation, exchange acidity, and effective cation exchange capacity, and some relevant cation ratios. This strategy also allowed the identification of an ionic unbalance in the soil chemistry, due to an excess of Mg2+, that was harming crop health, as reported by NDVI. This also impacted ECa and NDVI relationship, which was negative in this study. Overall, the results demonstrate the advantages of using remotely sensed data, mainly more than one type of sensing data, and suggest a high potential for differential crop fertilization and soil management in the study area.