Abstract
Fano resonances emerge from the coherent interference between a discrete resonant state and a continuum of propagating modes, giving rise to a characteristically asymmetric spectral line shape with heightened sensitivity to minute variations in the underlying physical parameters. This study provides a chronological perspective on the development and increasing implementation of Fano-type effects in fiber-optic technology. Their implementation in fiber Bragg gratings (FBGs) through numerical simulations of resonant Fano behavior. Specifically, a Fano-like response in an FBG can be designed by introducing a tailored phase shift into the grating structure; the magnitude of this phase discontinuity constitutes an effective control parameter for tuning the interference condition and, consequently, the resulting spectral asymmetry. The resonance produces a distinctive, asymmetrical spectral response that is attractive for a broad range of photonic functionalities. Key applications include fiber-integrated sensing - where the enhanced spectral sensitivity supports the detection of changes in refractive index, temperature, pressure, or biomolecular interactions - as well as narrowband filtering and spectral shaping for telecommunications and optical signal-processing systems.

Abstract
The growing integration of Artificial Intelligence (AI) into interactive systems presents unique challenges and opportunities for Human-Computer Interaction (HCI) and User Experience (UX). While AI can enhance usability and provide novel interaction paradigms, it also raises concerns related to transparency, control, and user trust. This workshop seeks to bring together researchers and practitioners to discuss state-of-the-art engineering methods that support HCI and UX in AI-driven systems. By fostering interdisciplinary collaboration, we aim to identify key challenges, share best practices, and develop a roadmap for future research in this critical area.

Abstract
Safety-critical interactive systems pose design and evaluation challenges that go beyond usability. The safety of the system (i.e. the guarantee that it does not reach an undesirable or incorrect state) is also a relevant consideration. Traditional user-centred approaches (UCD) lack the rigour and thoroughness needed to address safety, and formal verification arises as a possible solution. Applying formal verification to a safety-critical interactive system design encompasses developing a model, expressing and verifying properties, and analysing the verification results. In the case of model checking, properties are typically expressed in temporal logic. This creates a gap between the languages used in UCD and the languages used for formal verification. Creating temporal logic properties manually requires expertise in formal methods and can be both time-consuming and error-prone. This paper explores how a patterns-based approach can be used to support the specification of properties in a natural language-based style. A prototype implementation of the approach is evaluated through a user study, and the results of this evaluation are discussed.

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
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.