Abstract
Stand-level forest tree species perception and identification are needed for monitoring-related operations, being crucial for better biodiversity and inventory management in forested areas. This paper contributes to this knowledge domain by researching tree trunk types multispectral perception at stand-level. YOLOv5 and YOLOv8 - Convolutional Neural Networks specialized at object detection and segmentation - were trained to detect and segment two tree trunk genus (pine and eucalyptus) using datasets collected in a forest region in Portugal. The dataset comprises only two categories, which correspond to the two tree genus. The datasets were manually annotated for object detection and segmentation with RGB and RGB-NIR images, and are publicly available. The Small variant of YOLOv8 was the best model at detection and segmentation tasks, achieving an F1 measure above 87% and 62%, respectively. The findings of this study suggest that the use of extended spectra, including Visible and Near Infrared, produces superior results. The trained models can be integrated into forest tractors and robots to monitor forest genus across different spectra. This can assist forest managers in controlling their forest stands.

Abstract
This paper explores the use of Robotics and decentralized Multi-Agent Reinforcement Learning (MARL) for side-by-side navigation in Intelligent Wheelchairs (IW). Evolving from a previous work approach using traditional single-agent methodologies, it adopts a Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm to provide control input and enable a pair of IW to be deployed as decentralized computing agents in real-world environments, discarding the need to rely on communication between each other. In this study, the Flatland 2D simulator, in conjunction with the Robot Operating System (ROS), is used as a realistic environment to train and test the navigation algorithm. An overhaul of the reward function is introduced, which now provides individual rewards for each agent and revised reward incentives. Additionally, the logic for identifying side-by-side navigation was improved, to encourage dynamic alignment control. The preliminary results outline a promising research direction, with the IWs learning to navigate in various realistic hallways testing scenarios. The outcome also suggests that while the MADDPG approach holds potential over single-agent techniques for the decentralized IW robotics application, further investigation are needed for real-world deployment.

Abstract
Faced with the current unsustainability and recognizing the importance of engineering (and technology) in the Capitalocene, it is important to develop educational approaches that facilitate the awareness and training of engineering students to the sustainable future's construction. The main objective of the study is the evaluation of the educational approach developed (educational board game). It was used an action-research methodology and a quasi-experimental method. These results show that the developed game can be an important contribution in the engineers training to change the role of engineering to an ethical and responsible construction of a sustainable and fair future.

Abstract
Total arthroplasty is one of the most common knee surgeries and, due to the ageing of the population, the number of procedures performed each year is expected to increase. With almost a quarter of patients dissatisfied, systems for computer assistance in orthopaedic surgery have been on the rise, appearing to have better outcomes than conventional techniques by reproducing a planned alignment with a similar learning curve. The search for inexpensive solutions to improve these prototypes is extremely relevant since most systems in the market involve expensive robots. The development of a simulation for an extended reality system, specifically spatial augmented reality, with a projector and a depth camera to project the desired total knee arthroplasty bone cuts onto a simulated knee joint has been proposed. It was created with Gazebo and communicates with the Robotic Operating System (ROS) framework so that it can easily be transposed to the real world. An evaluation of the simulator was performed regarding the projection's accuracy. The performance of the simulator was fitting for surgery, with the highest mean position error between the desired bone cut and the simulated bone cut of 1.11 +/- 0.86 mm (minimum = 0.00 mm, maximum = 2.60 mm) for the tibia cut. These values could be further improved with the implementation of a feature-matching algorithm and a dynamic projection.

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Abstract