Abstract
Glaucoma is one of the leading causes of irreversible blindness worldwide, affecting millions of people, often silently and progressively. Early diagnosis is crucial to slow its progression, but it remains challenging due to the need for manual analysis of large volumes of retinal images by trained specialists. In this context, automatic detection systems based on deep learning offer a promising opportunity to facilitate and accelerate the diagnostic process, providing scalability and high accuracy. This work presents the development of an automatic method for optic disc and optic cup segmentation in retinal fundus photographs, aiming to support early glaucoma detection. The proposed methodology is based on convolutional neural networks (CNNs), specifically an enhanced U-Net architecture with a ResNet50 backbone, incorporating attention mechanisms and data augmentation strategies to improve segmentation accuracy. The model was trained and validated using the REFUGE dataset, which contains high-quality fundus images with manual annotations of the disc and cup regions. Experimental results demonstrate that the developed model achieved an average Dice coefficient of 0.937 for optic disc segmentation and 0.828 for optic cup segmentation. Analysis of the cup-to-disc ratio (CDR) yielded mean values of VCDR = 0.497 ± 0.059, ACDR = 0.252 ± 0.060, and mean CDR = 0.375 ± 0.058, with 55.0% of cases classified as low risk, 43.3% as moderate risk, and 1.7% as high risk for glaucoma. These results highlight the potential of the proposed method as an assistive tool for automated glaucoma screening.

Abstract
Product design is a complex and iterative process that requires the balance of multiple constraints, such as material selection, manufacturability, regulatory compliance, and structural integrity, among others. Traditional design workflows follow a human-driven approach, limiting efficiency, adaptability, and the ability to quickly respond to evolving limitations. This paper introduces an agentic approach to product design, leveraging multi-agent systems to distribute and automate design tasks dynamically. To demonstrate this methodology, a hypothetical enclosure design is used as a guiding example, demonstrating how agents interact to generate product specifications, select materials, validate structural properties, assess manufacturability, and perform other relevant tasks throughout the design process. To implement this framework, CrewAI is utilized as an agent coordination system that enables the structured definition of roles and execution of tasks for autonomous agents. In the final section, a case study is presented, focusing on the design of a parallelepiped enclosure, applying the proposed framework in a simulated environment. Our findings highlight the advantages of agent-based collaboration in product design, showcasing its potential to optimize workflows, reduce development time, and improve adaptability to changing requirements.

Abstract
Road safety has become an increasingly important concern and the integration of Advanced Rider Assistance Systems and Advanced Driver Assistance Systems plays a crucial role in preventing accidents. This work proposes a computer vision pipeline to automatically detect hazardous road anomalies—loose gravel, potholes, and puddles—from a motorcycle-mounted camera, targeting real-time operation on embedded edge devices. A hybrid dataset of 28764 annotated images was created by combining real-world photos, Blender-rendered synthetic scenes, and AI-generated images to improve diversity and coverage. Multiple state-of-the-art object detectors were trained and benchmarked, including the YOLOv5/7/11/12 families and the transformer-based RT-DETR architecture. While the RT-DETR model achieved the highest precision overall, its computational complexity and heavy resource requirements limited its suitability for real-time deployment on low-cost embedded platforms. Conversely, the YOLOv11n model demonstrated the best accuracy–efficiency trade-off, reaching mAP@0.5 = 0.872 at 320$\times$320 with 0.045 s/frame on a Jetson Nano, while lighter variants remained viable on Raspberry Pi boards. Across classes, gravel was the most reliably detected, and operating points around a confidence threshold of $\tau \approx 0.31$ yielded balanced F1 scores up to 0.82. Although results show that automatic road-condition monitoring on affordable hardware is feasible, the prototype has not yet undergone on-road field trials. It does not include an integrated rider alert module or energy-use assessment. These gaps define the immediate roadmap for deployment.

Abstract
Purpose This study aims to examine how artificial intelligence (AI) governance supports sustainable decision-making across organizational contexts in Europe, focusing on six Portuguese firms in energy, urban mobility and finance. Design/methodology/approach Adopting a sociotechnical perspective, this research uses a qualitative multiple case study design with semi-structured interviews of Chief Information Officers across diverse organizational contexts. It integrates technical and social dimensions to capture how digital infrastructures, governance practices and human factors interact in decision-making processes. Findings The findings reveal that governance increasingly aligns with formal frameworks through policies, dedicated structures, human oversight and Environmental, Social and Governance (ESG) oriented indicators, enhancing transparency and reliability. However, maturity varies by sector, resources and technology and challenges such as data limitations, organizational resistance and regulatory uncertainty persist. Furthermore, AI governance emerges as an adaptive, iterative capability for navigating sustainability complexities. Originality/value This study provides original insights by linking AI governance to sustainable decision-making through a sociotechnical lens, an area still underexplored in empirical research. It advances theory by integrating ESG considerations into AI governance and offers practical value by identifying mechanisms that enhance transparency, accountability and sustainability outcomes.

Abstract
Eye diseases, such as glaucoma, diabetic retinopathy, and age-related macular degeneration, drive the growing need for reliable and scalable analyses of fundus and optical coherence tomography (OCT) images. Deep learning performs strongly in ocular structure segmentation. However, it typically relies on dense pixel-wise annotations, which are costly and difficult to obtain at scale. Weakly supervised learning (WSL) can reduce this burden by leveraging coarse labels, limited strong annotations, and unlabeled data. This systematic umbrella review synthesizes survey and review articles on weakly supervised deep learning for image segmentation, with a focus on ocular imaging (fundus and OCT/OCTA). After analyzing twenty-one secondary studies, the main finding reveals an empty intersection: WSL-focused segmentation surveys are often modality-agnostic. Conversely, ocular reviews are predominantly fully supervised and seldom offer quantitative evidence on annotation-effort savings or direct comparisons between weak and fully supervised methods on identical datasets. Across the included reviews, label-efficient strategies cluster around CAM/MIL formulations, sparse supervision (points/scribbles/boxes), pseudo-labelling/self-training, and semi-/self-supervised learning, implemented mainly with U-Net/DeepLab families and increasingly Transformer or hybrid backbones. These results provide a structured map of available WSL mechanisms and, critically, identify reproducible reporting gaps that currently prevent fair benchmarking in ocular segmentation. Therefore, this review supports the development of ocular-specific benchmarks and minimum reporting practices that link segmentation performance to annotation effort.

Abstract
Efficient battery management in electric vehicles plays a key role in the transition to more sustainable and energy efficient mobility. This article presents a proposal for a modular framework to optimise charging and energy consumption based on solar radiation prediction. The solution integrates three main components: climate prediction models, battery behaviour simulation, and optimisation algorithms for decision making. This approach aims to dynamically adapt charging strategies to maximise vehicle autonomy and reduce energy waste. The modularity of the framework allows it to be applied to different vehicle types and operating contexts, ensuring flexibility and scalability. In addition, preliminary studies on solar radiation forecasting have already been carried out, providing a basis for future development of the system. The implementation of this approach represents an important step towards more efficient energy management in electric vehicles, contributing to the reduction of environmental impact and the promotion of sustainable electric mobility.