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
Abstract This article examines the ethical dimensions of AI-supported fact-checking tools in the context of elderly health care, focusing on how caregivers interpret trust, credibility, and cultural relevance when engaging with AI-mediated information. The study adopts a qualitative, exploratory methodology grounded in Value Sensitive Design and Human–Machine Communication. Empirical data were collected through a co-design workshop, focus group discussions, empathy map exercises, and descriptive questionnaires with elderly caregivers in a rural Portuguese context. Rather than aiming for statistical generalization, the research prioritizes contextual understanding and value articulation. The findings reveal that caregivers evaluate AI-supported fact-checking solutions not only in terms of informational accuracy, but also through ethical considerations such as protection, inclusion, trustworthiness, and perceived legitimacy. Differences between traditional journalistic approaches, automated AI solutions, and visual or animated formats were less salient than participants’ interpretations of how these tools aligned with their lived experiences and communicative norms. By foregrounding the perspectives of caregivers in a socioeconomically constrained setting, this study contributes empirically grounded insights to debates in AI ethics and responsible AI design. It highlights the importance of culturally sensitive, user-centered approaches when deploying AI technologies in health communication and misinformation management, particularly for vulnerable populations.

Abstract
While Small and Medium-sized Enterprises (SMEs) in emerging markets have been adopting advanced digital technology to gain competitiveness and scale, they still face business cybersecurity risks due to limited resources, awareness, and expertise. This paper aims to systematically review the literature covering cybersecurity, conceptual frameworks, and challenges associated with Digital Transformation (DT) faced by Small and Medium-sized Enterprises (SMEs) in developing countries, with specific emphasis in Mozambique. The established inclusion criteria were papers published between 2015 to 2024, in English and Portuguese, focusing on cybersecurity, cybersecurity framework, Digital Transformation (DT), Small and Medium-sized Enterprises (SMEs) of any sector, and developing countries with emphasis of Mozambique or similar contexts. Studies related to large enterprises, and research published more than 10 years were excluded. A total of 22 out of 124 research articles, journals, and conferences, were analyzed in detail, according to bibliographic information, different research design, outcomes, and findings. The study used four digital libraries, namely: IEEE Xplore, American Computing Machinery (ACM), Science Direct, SCOPUS, and Google Scholar were used in addition. The quality assessment checklist was used in the included studies to evaluate the methodological rigor and relevance of primary studies. The findings reveal that even as the world becomes more aware of the need for Digital Transformation and cybersecurity, Small and Medium-sized Enterprises (SMEs) in developing countries continue to be underserved. This review allowed to identify a considerable disparity between the cybersecurity practices applied by SMEs in developing countries and the guidelines provided by international frameworks. The review emphasizes important areas that warrant longitudinal, sector-specific, impact-driven research, policy-oriented studies that can inform scalable, inclusive, and sustainable digital strategies, as well as a dedicated cybersecurity framework appropriate for the Mozambican context.

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

Abstract
Federated learning has emerged as a key paradigm for distributed optimization under privacy and communication constraints, yet classical aggregation schemes such as Federated Averaging (FedAvg) assume uniform client reliability and remain vulnerable to corrupted, noisy, or malfunctioning participants. This limitation is particularly critical in distributed sensing systems, where clients collect partial and heterogeneous observations of a shared physical signal. In this work, we propose a Trust-Aware Federated Signal Reconstruction (TAFSR) framework for robust federated linear inverse problems under client-level corruption. Unlike generic robust aggregators designed for update-space filtering, the proposed method introduces a residual-consistency trust mechanism tailored to inverse problems with partial observations, and admits both a block-wise IRLS interpretation and a spectral-norm error analysis. We formalize federated reconstruction as a distributed least-squares problem with unreliable clients and show that, under strong convexity and weighted-operator spectral bounds, the method converges linearly up to a corruption-dependent bias term. Under persistent residual separation between clean and corrupted clients, the idealized unclipped weighting rule further yields asymptotic attenuation of corrupted-client influence relative to uniform aggregation. The analysis also establishes spectral error bounds and clarifies the connection between trust-aware aggregation and robust M-estimation. Extensive reproducible experiments on industrial vibration data (CWRU) and clinical ECG datasets (MIT-BIH and PTB-XL) under heterogeneous noise and simulated corruption demonstrate that TAFSR consistently improves robustness over FedAvg. Under the default 20% corrupted-client setting, the mean final RMSE decreases from 0.3100 to 0.1999 on CWRU and from 0.2809 to 0.1815 on MIT-BIH, while the method also maintains an advantage on the more challenging low-redundancy PTB-XL regime. These results position TAFSR as a reproducible and task-aware alternative to uniform or generic update-space aggregation for distributed signal reconstruction in heterogeneous sensing environments.