Abstract
Retinal diseases such as glaucoma, diabetic retinopathy, and age-related macular degeneration affect hundreds of millions of people worldwide and are among the leading causes of vision loss. Optical Coherence Tomography (OCT) is a non-invasive imaging technique widely used to support the diagnosis of these conditions. However, manual analysis of OCT images is time-consuming, prone to inter-observer variability, and requires extensive clinical expertise. In recent years, deep learning methods have shown outstanding performance in medical image segmentation tasks. This work proposes an automatic approach for the segmentation of retinal layers in OCT images using the GOALS 2022 dataset. Four segmentation architectures were evaluated - U-Net, DeepLabV3+, FPN (U-Net++), and Attention U-Net - all combined with the ResNet50 encoder. Additionally, the influence of encoder selection in the U-Net architecture was investigated, testing ResNet34, EfficientNetB0, MobileNetV2, VGG16, and InceptionV3. The results show that the DeepLabV3+ model achieved the best overall performance, with an F1-Score of 0.9669 and an IoU of 0.9370. These findings demonstrate that lightweight, accessible models can achieve results comparable to state-of-the-art methods, offering a promising solution for clinical applications in retinal image segmentation. © 2025 The Authors. Published by Elsevier B.V.

Abstract
Motorcycle accidents contribute significantly to road traffic fatalities worldwide. Advanced Rider Assistance Systems are systems designed to improve safety by providing real-time monitoring, collision detection, and adaptive control. However, implementing these systems on low-power hardware poses some challenges. This work examines the usage of object detection models on edge computing devices, with a focus on the Raspberry Pi 4. In this work are evaluated some models like SSD, and YOLO models using a custom dataset based on BDD100K dataset. All the models used in the project were converted to Open Neural Network Exchange format and tested at resolutions of 320 x 320 and 640 x 640 to assess their efficiency and real-time applicability. The results indicate that YOLO-based models, particularly YOLOv11, give the best balance between accuracy and inference speed, making them a candidate for Advanced Rider Assistance Systems applications. Despite these advancements, challenges such as real-time constraints and hardware limitations remain. Future research should focus on model quantization and hardware acceleration to improve deployment feasibility.

Abstract
Engineering is considered important in solving unsustainability. However, it is a complex problem that must be viewed, analysed and studied from various perspectives and taking with the contribution of various areas of knowledge. This work studied the use of interdisciplinarity as a contribution to interconnect ethics and sustainability with technical-scientific contents of electrical engineering. The research intended to use interdisciplinarity to help engineering students recognise that engineering is not ethically neutral, and that, therefore, the problems (and solutions) must also be analysed from an ethical and sustainability perspective. A framework was developed, and a pedagogical activity using interdisciplinarity was applied. Results show that, after the activity, students recognise that ethical values influence calculations in the area of electrical installations; and move from a single view to identify different alternatives, perspectives, motivations and multiple objectives. This leads to studying more alternatives and hopefully better overall technical solutions.

Abstract
High-resolution UAV imagery offers unprecedented opportunities for vineyard monitoring, yet its practical use in semantic segmentation is hindered by the high cost of pixel-level annotation. Weakly supervised learning (WSL) emerges as a promising alternative, capable of reducing annotation effort while preserving competitive performance. In this study, we conduct a direct comparative evaluation of two pseudo-labelling strategies for vine row segmentation, a task still underexplored in perennial crops. The first strategy combines a spectral heuristic with Conditional Random Fields (CRF) to enforce spatial consistency, while the second employs token clustering of DINO-ViT embeddings. To ensure fairness, both pseudo-label sets were used to train an identical segmentation architecture (U-Net with ResNet50), thereby isolating the impact of pseudo-label quality. Results, measured by precision, recall, F1-score, and Intersection over Union (IoU), reveal that the CRF-refined heuristic (F1 = 0.77, IoU = 0.62) consistently outperforms the transformer-based clustering approach (F1 = 0.52, IoU = 0.50). These findings highlight the decisive role of spatial regularisation in weak supervision and provide a reproducible pipeline that balances accuracy, methodological simplicity, and annotation cost. The contribution of this work lies in demonstrating a practical and extensible framework for UAV-based vineyard monitoring, while opening pathways for hybrid approaches that integrate semantic depth with spatial coherence in future research.

Abstract
This article presents a Ph.D. research proposal for the automation of Digital Twin construction in industrial contexts through the semantic integration of heterogeneous data. The approach combines Large Language Model with the Asset Administration Shell framework to extract and map technical information from structured and unstructured sources (such as sensors, manuals and ERP/MES systems) into standardized submodels. The methodology includes four stages: data collection, semantic mapping using, organization into submodels and integration into Digital Twins. Initial tests with simulated data show the ability of LLMs to identify equivalent technical terms and generate structured data compatible with Asset Administration Shell. Ongoing work includes future activities with data from industrial partners, development of evaluation metrics and analysis with domain experts. The aim is to reduce manual modeling work, support interoperability and enable the construction of scalable Digital Twin in line with Industry 4.0 frameworks.

Abstract
Traffic safety is a significant issue, particularly for motorcyclists, who are significantly more vulnerable to fatal accidents compared to drivers of enclosed vehicles. Advanced Rider Assistance Systems have the potential to improve motorcyclist safety by mitigating critical risks like rear-end collisions. This paper proposes an integrated Advanced Rider Assistance Systems architecture using the RTDETR object detection model to predict rear-end collisions and provide real-time alerts to the riders. Specifically, it aims to mitigate critical risks like rear-end collisions, offering a comprehensive safety solution. The experimental data indicate that the proposed system is suitable for diverse operational environments, supporting the development of advanced sensing and alert systems to improve motorcyclist safety. Our system achieves an average precision (AP) of 0.688 at IoU=0.50 and reduces collision risk by issuing timely warnings. Contributions include the integration of RTDETR for improved detection accuracy, a multi-threshold warning mechanism, and a detailed analysis of system performance under varied conditions.