Abstract
Object detection is a fundamental task of computer vision that is constantly evolving, with a wide range of applications in fields such as security, medicine, and autonomous driving. This work presents an interactive self-learning course dedicated to exploring some crucial concepts for beginners in object detection. The course offers educational resources, including the possibility to follow a simple tutorial on the operation of an object detection model and definitions of the main concepts related to object detection technology. Users also have a brief description of object detection algorithms such as YOLO (You Only Look Once), R-CNN (Region-based Convolutional Neural Networks), and SSD (Single Shot Detector) and the possibility to learn more about these in a tutorial prepared on a Google Colab notebook. The course aims to provide a learning experience accessible to beginners in the field of object detection, who want to take the first step in their learning about the subject. After completing the tutorial, the user answers a questionnaire, with the goal of analyzing the learning outcomes and extracting the user's impression of the website in general. With this paper, we want to show the advantages of using tools of this nature to foster learning regarding object detection.

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Abstract
The footwear industry, a sub-sector of textile industrial sector, faces increased pressures towards higher levels of sustainability and circularity along all the value chain. Along the last decades, shoe products have become more complex products, integrating a greater number of components, materials diversity and often long supply-chains related to cost reduction and production or sourcing delocalization strategies. Full value-chain digitalization, as a cornerstone of Industry 4.0 paradigm, plays a key role for leveraging more sustainable and circular products, namely by traceability operationalization and forthcoming instruments such as Digital Product Passport. This research studied, via a state-of-art framing of the challenges followed by qualitative approach, how Industry 4.0 technologies can support the development of new services that contribute to sustainable and circular practices in footwear companies. An interview-based survey was conducted to 6 footwear companies, to map the adoption level of Industry 4.0 technologies and cross-linking to circular services business models.

Abstract
This work presents the development of low-cost, low-power, and disposable marine monitoring technologies designed to support oceanographic studies in remote and extreme environments. These platforms were initially targeted for underwater volcanic locations but offer a broader application potential for ocean research. Three main technologies were developed and tested: underwater monitoring probes for real-time water quality assessments in geothermal springs, deep-sea probes for vertical ocean profiling and autonomous drift-phase monitoring, and a surface buoy for rapid-response environmental monitoring. Field deployments in diverse locations, including the Ponta da Ferraria (S. Miguel, Azores), Banco D. Joao de Castro (Atlantic Ocean), and the Cumbre Vieja volcanic eruption site (La Palma, Canarias), demonstrated the operational feasibility of these systems. Despite challenges in deep-sea operation and deployment conditions, the results highlight the potential of these platforms for scientific studies, environmental monitoring, and emergency response. Their adaptability and modularity make them valuable tools for a wide range of oceanographic applications beyond their initial focus. Ongoing efforts to improve marine communication reliability, sensor integration, and resilience to extreme ocean conditions hold the potential to further expand the role of these technologies in marine exploration.

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[No abstract available]

Abstract
The integration of robot manipulators into additive manufacturing processes, particularly in fused filament fabrication, presents opportunities to overcome limitations of traditional three-axis systems. By leveraging the additional degrees of freedom, more versatile and efficient manufacturing solutions can be developed. However, this increased complexity introduces new challenges, including the need for trajectory planning that accounts for reachability, singularities, collision avoidance, and material deposition in various build orientations. This study focuses on the development and evaluation of trajectory generation approaches for robotic FFF using an ABB CRB 15000 manipulator. All approaches began with the same G-code input, and tests were conducted both in simulation and on the real robot. The results were analyzed in terms of trajectory accuracy, joint speed and acceleration profiles, parameters influence, and the quality of the printed parts.