Abstract
With the rise of Transformers, Vision Transformers (ViTs) have become a new standard in visual recognition. This has led to the development of numerous architectures with diverse designs and applications. This survey identifies 22 key ViT and hybrid CNN-ViT models, along with 5 top Convolutional Neural Network (CNN) models. These were selected based on their new architecture, relevance to benchmarks, and overall impact. The models are organised using a defined taxonomy formed by CNN-based, pure Transformer-based, and hybrid architectures. We analyse their main components, training methods, and computational features, while assessing performance using reported results on standard benchmarks such as ImageNet and CIFAR, along with our training and fine-tuning evaluations on specific imaging datasets. In addition to accuracy, we look at real-world deployment issues by analysing the trade-offs between accuracy and efficiency in embedded, mobile, and clinical settings. The results indicate that modern CNNs are still very competitive in limited-resource environments, while advanced ViT variants perform well after large-scale pretraining, especially in areas with high variability. Hybrid CNN-ViT architectures, on the other hand, tend to offer the best balance between accuracy, data efficiency, and computational cost. This survey establishes a consolidated benchmark and reference framework for understanding the evolution, capabilities, and practical applicability of contemporary vision architectures.

Abstract
This work presents an experimental framework for offshore seismic monitoring that combines Distributed Acoustic Sensing (DAS) with ocean-bottom seismometers (OBS). The study was conducted in the Azores region - Faial, where an HDAS interrogator prototype was connected to dark fiber submarine fiber-optic cable, complemented by the installation of two Ocean Bottom Seismometers (OBS) for calibration and validation of DAS technology. The main objective is to demonstrate that seismic observations obtained by DAS from seafloor cables can provide essential information similar to OBS and particularly in areas where land-based monitoring stations are limited.

Abstract
Waste management networks are essential for environmental protection and public health, but vulnerable to regulatory evasion, fraud, and illegal trading. Detecting potentially illicit activities in the network requires robust anomaly detection systems. However, the complexity of interactions between heterogeneous entities such as recycling companies, individuals, and other organisations combined with temporal irregularities and network dynamics makes conventional fraud detection approaches less effective. In this study, we introduce a dynamic graph-based framework that combines statistical change detection methods, the Page-Hinkley and CUSUM tests, alongside a deep learning model, LSTM-VAE, to detect suspicious activities in the Portuguese waste management network. Using real-world waste transfer records, we engineered temporal and network features to reveal a wide range of anomalies, including abrupt shifts in activity and unusual connectivity patterns, such as those involving collusive triangles. The evaluation was based on four pre-labeled anomalous companies identified by regulators. Our results show that while individual methods excel at detecting certain behaviors, their combination provides robust coverage of diverse anomaly types, with each anomalous company identified by at least three techniques. This approach demonstrates the importance of integrating temporal and network-based analysis, offering regulatory authorities a scalable tool to prioritize inspections, enhancing accountability in the waste management network.

Abstract
The segmentation of the foveal avascular zone (FAZ) in optical coherence tomography angiography (OCTA) images plays a crucial role in diagnosing and monitoring ocular diseases such as diabetic retinopathy (DR) and age-related macular degeneration (AMD). However, accurate FAZ segmentation remains challenging due to image quality and variability. This paper provides a comprehensive review of FAZ segmentation techniques, including traditional image processing methods and recent deep learning-based approaches. We propose two novel deep learning methodologies: a multitask learning framework that integrates vessel and FAZ segmentation, and a conditionally trained network that employs vessel-aware loss functions. The performance of the proposed methods was evaluated on the OCTA-500 dataset using the Dice coefficient, Jaccard index, 95% Hausdorff distance, and average symmetric surface distance. Experimental results demonstrate that the multitask segmentation framework outperforms existing state-of-the-art methods, achieving superior FAZ boundary delineation and segmentation accuracy. The conditionally trained network also improves upon standard U-Net-based approaches but exhibits limitations in refining the FAZ contours.

Abstract

Abstract
Digital platforms transform children’s relationships with media through algorithmic marketing strategies targeting Generation Alpha. This ethnographic study explores how digital platforms shape children’s musical cultures through algorithmic recommendation systems and commercial content strategies. Focusing on 22 children aged 8–11 in northern Portugal, drawing-talks and participant observation reveal the emergence of musicmedia, a hybrid convergence of music, visuals, platforms, and social practices, amid tensions between algorithmic prescription and children’s creative agency. Key findings demonstrate TikTok as primary discovery engine, funneling children through personalized recommendation sequences toward sustained YouTube and Spotify engagement. Children actively curate playlists, optimize content trajectories, and reappropriate viral fame narratives, transforming platform logics into spaces for identity experimentation and peer validation. Algorithms serve dual functions: prescriptive content delivery optimizing engagement conversion and aspirational narratives fostering long-term retention. Theoretically, results advance understanding of musicmedia as sociocultural phenomenon negotiating children’s imaginative resistance against platform architectures. Findings challenge protectionist approaches, highlighting sophisticated digital competencies among Generation Alpha. Implications inform music marketing strategies leveraging virality-sequencing, authentic aspiration amplification, and algorithmic transparency within children’s digital ecosystems.