Abstract
Over the years, industrial evolution has proved to be a complex process, since there are several aspects that need to be considered to achieve highly functional processes and differentiated quality products. To date, four industrial revolutions have been implemented. Thus, the paradigm of Industry 4.0 (I4.0) was born, a concept that aims to improve the efficiency, productivity, automation, and safety of industrial processes, but which also considers the operator’s relevance and centrality in these processes. Besides these four revolutions one more concept is emerging, called Industry 5.0 (I5.0). In recent years, and with the advance of scientific research, the implementation of wearables has proven to be the ideal solution to move towards the digitisation of Industrial sector. In this sense, the aim of this work is to provide a systematic review on the currently available knowledge about wearable technology and its applicability within I4.0. Through these technologies, both processes and operators can be monitored in real time, actively contributing to the identification of limitations and to the implementation of improvements. On the other hand, studies on the acceptance of these devices have shown a certain apprehension by users regarding the security and privacy of collected data. Therefore, studies should be conducted to analyse in depth these limitations, to raise users’ confidence and contribute, in a broader perspective, to the success of industrial processes. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
This paper presents a thorough analysis on the temperature dependence of the thermo-optic coefficient, dn/dT, of four bulk annealed pure-silica glass samples (type I-natural quartz: Infrasil 301; type II-quartz crystal powder: Heraeus Homosil; type III-synthetic vitreous silica: Corning 7980 and Suprasil 3001) from room temperature down to 0 K. The three/four term temperature dependent Sellmeier equations and respective coefficients were considered, which results from fitting to the raw data obtained by Leviton et al. The thermo-optic coefficient was extrapolated down to zero Kelvin. We have obtained dn/dT values ranging from 8.16 x 10(-6) up to 8.53 x 10(-6) for the four samples at 293 K and for a wavelength of 1.55 & mu;m. For the Corning 7980 SiO2 glass, the thermo-optic coefficient decreases monotonically, from 8.74 x 10(-6) down to 8.16 x 10(-6), from the visible range up to the third telecommunication window, being almost constant above 1.3 & mu;m. The Ghosh's model was revisited, and it was concluded that the thermal expansion coefficient only accounts for about 2% of the thermo-optic coefficient, and we have obtained an expression for the temperature behavior of the silica excitonic bandgap. Wemple's model was also analyzed where we have also considered the material dispersion in order to determine the coefficients and respective temperature dependences. The limitations of this model were also discussed.

Abstract
Structures associated with offshore wind energy production require an arduous and cyclical inspection and maintenance (O&M) procedure. Moreover, the harsh challenges introduced by sub-sea phenomena hamper visibility, considerably affecting underwater missions. The lack of quality 3D information within these environments hinders the applicability of autonomous solutions in close-range navigation, fault inspection and intervention tasks since these have a very poor perception of the surrounding space. Deep learning techniques are widely used to solve these challenges in aerial scenarios. The developments in this subject are limited regarding underwater environments due to the lack of publicly disseminated underwater information. This article presents a new underwater dataset: NEREON, containing both 2D and 3D data gathered within real underwater environments at the ATLANTIS Coastal Test Centre. This dataset is adequate for monocular depth estimation tasks, which can provide useful information during O&M missions. With this in mind, a benchmark comparing different deep learning approaches in the literature was conducted and presented along with the NEREON dataset.

Abstract
The successful adoption of Industry 4.0 technologies by firms requires them to formulate a digital strategy and implementation roadmap. An established approach to assess firms' needs towards digitalization is through maturity models. While there is a large number of maturity models in the literature, they present several limi-tations related to their generalizability and theoretical foundations. Our study aims to build and empirically validate an Industry 4.0 digital maturity model, based on the Technology-Organization-Environment framework. We conducted a systematic literature review of 55 digital maturity models, which we synthesized to create an integrated digital maturity assessment model. We tested our model through a focus group with industry experts and 24 companies from various manufacturing sectors. Our review suggests that existing digital maturity models have underestimated the relevance of the Environment dimension. Our empirical data suggests that companies often invest in digital technologies without considering critical organizational and environmental constraints.

Abstract
Coupled multi-scale finite element analyses have gained traction over the last years due to the increasing available computational resources. Nevertheless, in the pursuit of accurate results within a reasonable time frame, replacing these high-fidelity micromechanical simulations with reduced-order data-driven models has been explored recently by the modelling community. In this work, two classes of machine learning models are trained for a porous hyperelastic microstructure to predict (i) whether the microscopic equilibrium problem is likely to fail and (ii) the stress-strain response. The former may be used to identify critical macroscopic points where one may fall back to the high-fidelity analysis and possibly apply convergence bowl-widening techniques. For the latter, both a linear regression with polynomial features and artificial Neural Networks have been used, and the required stress-strain derivatives for solving the equilibrium problem have been derived analytically. A weight regularisation is introduced to stabilise the tangent operator and several strategies are discussed for imposing null stresses in undeformed configurations for both regression models. The regression techniques, here analysed exclusively in the context of porous hyperelastic materials, evidence very promising prospects to accelerate multi-scale analyses of solids under large deformation.

Abstract
ABSTRACT This research investigates the use of playful resources structured in Power Point for the bilingual education of deaf students in a school located in the municipality of Timon, in the state of Maranhão. This study aims to analyze how digital games contribute to language acquisition. Methodologically, this is a exploratory and qualitative field research. Class observation, a semi-open questionnaire, and bibliographic research were carried out for data collection and analysis. Results show that the games used promote engagement and motivation for learning. We also observed that the presence of deaf cultural elements creates a more empathetic and inclusive school environment.