Abstract
Business process automation (BPA) promises efficiency and quality gains, yet many initiatives—especially in small and medium-sized enterprises (SMEs)—fail to deliver because decisions are made in a fragmented, tool-led manner and with insufficient attention to organizational readiness. This study addresses that gap by developing and evaluating M4BPA. This technology-agnostic, lifecycle methodology supports BPA from opportunity identification through implementation and sustained operation. Following a Design Science Research approach, design requirements were derived from a structured review of BPA and related automation research and translated into an actionable method operationalized by four decision-support tools: (1) process selection, (2) prioritization, (3) digital readiness assessment, and (4) automation-technology decision support (including AI-enabled options where appropriate). M4BPA is evaluated through a multi-site demonstration in 16 SMEs, analyzing 63 candidate processes, of which 22 were automated, complemented by SME feedback via questionnaire and an expert focus group spanning business, IS, and technology perspectives. Results indicate that M4BPA improves decision quality by making selection criteria explicit, aligning automation choices with SME constraints, and institutionalizing governance mechanisms that mitigate common failure modes (e.g., misalignment, unclear roles, and unmanaged change). The study contributes a validated socio-technical BPA lifecycle artifact for SMEs and provides design knowledge for IS scholars and actionable guidance for managers seeking sustainable automation outcomes.

Abstract
Diabetic retinopathy (DR) is a vision-threatening complication of diabetes and one of the leading causes of blindness worldwide. It is characterized by the appearance of lesions on the retina, such as microaneurysms, hemorrhages, hard exudates, and soft exudates, which are crucial for staging the disease. Diagnosis is typically performed through analysis of fundus images, a manual process that is time-consuming and prone to subjectivity. To address this, this study explores the automatic segmentation of DRrelated lesions using deep learning techniques. Four convolutional neural network architectures were evaluated: U-Net, FPN, DeepLabV3+, and Attention U-Net. The IDRiD dataset was used for training and validation The DeepLabV3+ model with ResNet50 achieved the highest overall performance, while FPN was the only model capable of detecting microaneurysms in the multiclass task. These findings underscore the importance of architecture selection, loss function design, and preprocessing choices. Future work may explore new datasets, enhanced data augmentation, and the impact of optic disc removal on segmentation accuracy.

Abstract
Motorcycle safety is significantly compromised by blind spot collisions, necessitating Advanced Rider Assistance Systems. This paper proposes a blind spot warning system designed for edge devices, leveraging computer vision techniques for real-time object detection and tracking. The system aims to enhance rider awareness by detecting vehicles in blind spots and providing timely warnings through visual LED alerts. Preliminary results from training the RF-DETRBase model, the lightest version of the RF-DETR architecture, on the challenging BDD100K dataset, which features diverse driving scenarios and numerous small objects, demonstrate the system's potential. The dataset's inherent complexities were highlighted by poor small object detection (mAP 0.128) and declining performance at higher IoU thresholds. Despite these challenges, the model achieved a promising Average Precision of 0.700 at an Intersection over Union of 0.50, indicating effective vehicle detection.

Abstract
One of the key aspects of Industry 4.0 is using intelligent systems to optimize manufacturing processes by improving productivity and reducing costs. These systems have greatly impacted in different areas, such as demand prediction and quality assessment. However, the prognostics and health management of industrial equipment is one of the areas with greater potential. This paper presents a comparative analysis of deep learning architectures applied to the prediction of the remaining useful life (RUL) on public real industrial datasets. The analysis includes some of the most commonly employed recurrent neural network variations and a novel approach based on a hybrid architecture using transformers. Moreover, we apply explainability techniques to provide comprehensive insights into the model's decision-making process. The contributions of the work are: (1) a novel transformer-based architecture for RUL prediction that outperforms traditional recurrent neural networks; (2) a detailed description of the design strategies used to construct the models on two under-explored datasets; (3) the use of explainability techniques to understand the feature importance and to explain the model's prediction and (4) making models built for reproducibility available to other researchers.

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.