Abstract
The eDNA autonomous biosampler results from a line of research aimed at developing systems for sampling and collecting marine biological data, and for collecting environmental DNA. Environmental DNA is a tool that has been increasingly used in the biological monitoring of aquatic environments, as it is a non-invasive method with very promising results when it comes to assessing biological diversity. In this sense, the automation of this method has the potential to greatly increase the temporal and spatial resolution of current biological monitoring programs in aquatic environments. The system has been developed in a partnership between research teams at the Centre for Robotics and Autonomous Systems (CRAS - INESC TEC) and CIIMAR and has been tested in multiple operational scenarios, including the Arctic, where it was attached to the AUV IRIS.

Abstract
The Czech Republic is home to the Hranice Abyss, the world's deepest natural underwater cave, a site extensively explored by a dedicated team of divers from a speleology group. Over the years, numerous studies have been conducted to unravel the cave's mysteries, delving into fields such as biology, hydrogeology, and geology. Mapping a cave of such vast dimensions and staggering depth poses formidable challenges, making the task hazardous, demanding, and timeintensive for a limited team of divers. In July 2022, the UNEXUP project was invited to explore and map the cave with its robot (UX1-neo), which contains many acoustic and optical sensors, used for navigation, localization, and mapping. Its unique control and dynamics allow the robot to successfully navigate through caves and flooded mines. This paper delves into the specifics of the six days of mission dives, offering insights into the mapping process, and presenting some of the results obtained from the entire cave.

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
Knowledge models often use hierarchical structures, which help break down complex data into manageable components. This enables better understanding and aids in reasoning and decision-making. Hierarchical structures are effective in organizing, managing, and processing complex information. Traditional Self-Organizing Maps are typically flat, two-dimensional grids for visualizing and grouping data. They can be shaped into hierarchical structures, offering benefits such as improved data representation, scalability, enhanced grouping and visualization, and hierarchical feature extraction while preserving data topology. This paper introduces a self-organizing hierarchical map with an appropriate topology and a suitable learning mechanism for retaining information in an organized way. In this conceptual model, information is selectively absorbed in each layer. These characteristics make the Hierarchical Self-organising Maps a powerful non-linear classifier. Simulations are conducted to test and evaluate the performance of this neural structure as a classifier. © 2024 IEEE.

Abstract
The step response of first-order systems is vital in control systems and electronics. Understanding this behaviour is key but often challenging. This article uses Arduino with PWM to teach the step response in RC circuits, since Arduino enables real-time data acquisition and visualisation, connecting theory to practice. The research seeks to illustrate the step response of an RC circuit using Arduino, deepen knowledge of first-order systems, and offer a technique for collecting experimental data. All of this, since combining practical experiments with theoretical concepts boosts student involvement and understanding of dynamic systems. The work includes theoretical foundations, experimental procedures, and a brief discussion on the educational value of these activities.

Abstract
The resting brain has been extensively investigated for low frequency synchrony between brain regions, namely Functional Connectivity. However the other main stream of the brain connectivity analysis that seeks causal interactions between brain regions, Effective Connectivity, has been still little explored. Inherent complexity of brain activities in resting-state, as observed in Blood Oxygenation-Level Dependant fluctuations, calls for exploratory methods for characterizing these causal networks [1]. To determine the structure of the network that causes this dynamics, it is developed a method of identification based on least squares, which assumes knowledge of the signals of brain activity in different regions. As there is no access to functional Magnetic Resonance Imaging, data it is developed a model to obtain the Blood Oxygenation Level Dependent signals and it is implemented a reverse hemo-dynamic function. To assess the performance of the created model Monte Carlo simulations have been used. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024.