Abstract
Objectives Space, an extreme environment, poses significant challenges to human physiology, including adverse effects on oral health (e.g., increase of periodontitis prevalence, caries, tooth sensitivity). This study investigates the differences in oral health routines and oral manifestations among analog astronauts during their daily routines and simulated space missions conducted on Earth. Materials and methods This research focused on scientist-astronaut candidates of the International Institute for Astronautical Sciences (IIAS) and analog astronauts from other institutions. The study used a cross-sectional methodology with a descriptive component. A total of 16 participants, comprising individuals aged between 21 and 55 years, were invited to complete an online questionnaire. A comparison was made between the subjects' oral hygiene practices in everyday life (designated as Earth in this research) and their oral hygiene routines during their space analog missions. Results (i) Toothbrushing duration was mostly 1-3 minutes (n = 13; 81.30% on Earth; n = 11; 68.80% on a mission); (ii) time spent was the greatest difficulty in maintaining oral hygiene routine on a mission (n = 9; 53,6%); (iii) There were more experienced oral symptoms on Earth (n = 12; 75%) than on mission (n = 7; 43.80%); (iv) The most frequent frequency of oral check-ups was > 12 months (n = 6; 37,5%); (v) Oral health materials were scarce on the mission (n = 9; 56.30%); (vi) For the majority, personal oral hygiene was classified as good (n = 9; 56.30% on Earth; n = 7; 43.80% on the mission). Conclusion and Clinical relevance This research contributes to increasing knowledge of oral hygiene measures in extreme environments, but further research is needed as this topic remains relatively understudied. This study represents an initial contribution to oral health in analog space missions, aiming to propose guidelines for future missions, including deep space missions and expeditions to extreme environments.

Abstract
In this study, different configurations based on linear fiber lasers were proposed and experimentally demonstrated to measure the concentration of liquid solutions. Samples of paracetamol liquid solutions with different concentrations, in the range from 52.61 to 201.33 g/kg, were used as a case-study. The optical gain was provided by a commercial bidirectional Erbium-Doped Fiber Amplifier (EDFA) and the linear cavity was obtained using two commercial Fiber Bragg Gratings (FBGs). The main difference of each configuration was the coupling ratio of the optical coupler used to extract the system signal. The sensing head corresponded to a Single-Mode Fiber (SMF) tip that worked as an intensity sensor. The results reveal that, despite the optical coupler used (50:50, 60:40, 70:30 or 80:20), all the configurations reached the laser condition, however, the concentration sensing was only possible using a laser drive current near to the threshold value. The configurations using a 70:30 and an 80:20 optical coupler allowed paracetamol concentration measurements with a higher sensitivity of (-3.00 +/- 0.24) pW/(g/kg) to be performed. In terms of resolution, the highest value obtained was 1.75 g/kg, when it was extracted at 20% of the output power to the linear cavity fiber laser configuration.

Abstract

Abstract
In the context of Industry 4.0, this paper explores the vital role of advanced technologies, including Cyber–Physical Systems (CPS), Big Data, Internet of Things (IoT), digital twins, and Artificial Intelligence (AI), in enhancing data valorization and management within industries. These technologies are integral to addressing the challenges of producing highly customized products in mass, necessitating the complete digitization and integration of information technology (IT) and operational technology (OT) for flexible and automated manufacturing processes. The paper emphasizes the importance of interoperability through Service-Oriented Architectures (SOA), Manufacturing-as-a-Service (MaaS), and Resource-as-a-Service (RaaS) to achieve seamless integration across systems, which is critical for the Industry 4.0 vision of a fully interconnected, autonomous industry. Furthermore, it discusses the evolution towards Supply Chain 4.0, highlighting the need for Transportation Management Systems (TMS) enhanced by GPS and real-time data for efficient logistics. A guideline for implementing CPS within Industry 4.0 environments is provided, focusing on a case study of real-time data acquisition from logistics vehicles using CPS devices. The study proposes a CPS architecture and a generic platform for asset tracking to address integration challenges efficiently and facilitate the easy incorporation of new components and applications. Preliminary tests indicate the platform’s real-time performance is satisfactory, with negligible delay under test conditions, showcasing its potential for logistics applications and beyond.

Abstract
Marine growth impacts the stability and integrity of offshore structures, while simultaneously preventing inspection procedures. In consequence, companies need to employ specialists that manually assess each impacted part of the structure. Due to harsh sub-sea environments, acquiring large quantities of quality underwater data becomes difficult. To mitigate these challenges a new data augmentation algorithm is proposed that generates new images by performing localized crops on regions of interest from the original data, expanding the total size of the dataset approximately 6 times. This research also proposes a learning-based algorithm capable of automatically delineating marine growth in underwater images, achieving up to 0.389 IoU and 0.508 Dice Loss. Advances in this area contribute for reducing the manual labour necessary to schedule maintenance operations in man-made submerged structures, while increasing the reliability and automation of the process.

Abstract
Reinforcement learning (RL) consists of designing agents that make intelligent decisions without human supervision. When used alongside function approximators such as Neural Networks (NNs), RL is capable of solving extremely complex problems. Deep Q-Learning, a RL algorithm that uses Deep NNs, has been shown to achieve super-human performance in game-related tasks. Nonetheless, it is also possible to use Variational Quantum Circuits (VQCs) as function approximators in RL algorithms. This work empirically studies the performance and trainability of such VQC-based Deep Q-Learning models in classic control benchmark environments. More specifically, we research how data re-uploading affects both these metrics. We show that the magnitude and the variance of the model's gradients remain substantial throughout training even as the number of qubits increases. In fact, both increase considerably in the training's early stages, when the agent needs to learn the most. They decrease later in the training, when the agent should have done most of the learning and started converging to a policy. Thus, even if the probability of being initialized in a Barren Plateau increases exponentially with system size for Hardware-Efficient ansatzes, these results indicate that the VQC-based Deep Q-Learning models may still be able to find large gradients throughout training, allowing for learning.