Abstract
Wireless Sensor Networks (WSNs) are increasingly evolving toward intelligent distributed systems in which local sensing, on-device inference, and collaborative model training are becoming central to scalable Internet of Things (IoT) deployments. However, the practical adoption of Federated Learning (FL) in WSN-oriented environments remains constrained by three major challenges: limited and unevenly depleted node energy, heterogeneous non-IID local data distributions, and variable client reliability during collaborative training. This paper proposes a Trust- and Energy-Aware Federated Learning (TEA-FL) framework specifically designed for resource-constrained WSN settings, in which client participation and server-side aggregation are jointly guided by residual energy estimates and dynamically updated trust scores. The proposed method prioritizes reliable, energy-efficient sensor nodes while reducing the impact of weakly aligned or low-quality local updates during global aggregation. The framework is evaluated on two representative WSN/IoT-oriented proxy benchmarks, Human Activity Recognition (HAR) and UNSW-NB15 intrusion detection, under both IID and Dirichlet-based non-IID federated partitions. Under non-IID HAR partitioning, TEA-FL improved final accuracy from 0.6752 with FedAvg to 0.7636 and final Macro-F1 from 0.5623 to 0.7185. On the more challenging non-IID UNSW-NB15 benchmark, TEA-FL achieved the highest final Macro-F1, 0.3711, compared with 0.3230 for FedAvg and 0.3323 for the trust-only baseline, although with a lower final accuracy. These results indicate that TEA-FL is particularly useful when final-round robustness, class-balanced behavior, and client sustainability are more relevant than maximizing a single peak intermediate accuracy value. Additional ablation and unreliable-client experiments further show that the trust-energy-aware aggregation component is particularly influential and that TEA-FL can improve behavior under selected low-quality participation scenarios, although it should not be interpreted as a complete Byzantine-robust defense. Overall, the findings suggest that jointly modeling update consistency and residual energy offers a practical, lightweight pathway toward more dependable and sustainable federated intelligence in next-generation WSN and IoT deployments.

Abstract
The polarization curve characteristics of proton exchange membrane (PEM) hydrogen electrolyzers lead to large variations in the equivalent load impedance over the operating current range. This results in a varying closed-loop system time response when traditional fixed-gain PI controllers are employed. In this work, the design and experimental validation of a 3-phase interleaved buck converter controlled via a proposed adaptive lead-lag current control strategy for a PEM hydrogen electrolyzer load is presented. The incremental load conductance method is used to obtain a control-oriented model of the converter-electrolyzer system, enabling real-time calculation of controller parameters via pole-zero cancellation and user-specified transient performance. A laboratory prototype is implemented to experimentally verify the approach under step-load changes, ramp-load changes, and 50% input voltage sag conditions. The results show less than 1% current ripple, identical transient performance over the entire operating range, and improved disturbance ride-through performance compared to a traditional PI controller. The proposed approach offers a viable and robust control solution for high-current PEM electrolyzer applications.

Abstract
Short-form destination videos often rely on music to carry cultural meaning. This paper links Cognitive Metaphor Theory with the circumplex dyad of pleasure and arousal to explain how music-image pairings build destination brand resonance (DBR). Three experiments show that pleasure is the stable route to DBR, arousal helps only under favorable tone, and their effects are additive. A Meaning-Access Prime (MAP) raises both emotions under identical clips and, in Bayesian structural models, also exerts a direct path to DBR, strongest when pleasant tone is low. DBR then predicts destination brand identification and destination consumption intention. We also show a useful state view: Resonant versus Emergent DBR. The framework provides design rules for co-tuning tone, activation, and cultural cues in creator-made clips that improve resonance, identification, and intention.

Abstract
The widespread adoption of embedded systems has led to their deployment in critical real-world applications, making them attractive targets for malicious actors. This paper presents RunPBA, a hardware-based runtime attestation system designed to defend against control flow attacks while maintaining minimal performance overhead and adhering to strict power consumption constraints. RunPBA leverages Pointer Authentication and Branch Target Identification (PACBTI), a new processor extension tailored for the ARM Cortex M processor family, allowing robust protection without requiring hardware modifications, a limitation present in similar solutions. We implemented a proof-of-concept and evaluated it using two benchmark suites, Coremark PRO and BEEBS. Experimental results indicate that RunPBA imposes a geometric mean performance overhead of only 1.3% and 6.8% across the benchmarks, underscoring its efficiency and suitability for real-world deployment.

Abstract
As urban populations swell, cities face mounting crises such as traffic congestion, waste management, pollution, and parking allocation. The advent of the smart city concept offers solutions to these challenges by leveraging the Internet of Things (IoT) and big data technologies. However, developing smart city technologies is difficult for cities with different infrastructural foundations. It is crucial to determine whether all cities, regardless of size or location, can align with the smart city concept to promote social equity across regions. The digital divide between large cities and low-density areas poses significant challenges for smaller towns and rural regions, making the transition to smarter environments more complex. This research focuses on developing smart cities in low-density areas, based on a case study of the Tras-os-Montes region in northern Portugal, examining the adoption and acceptance of smart city technologies in these regions. By constructing a smart city technology acceptance model, this study investigates the factors influencing the construction of smart cities in low-density areas, aiming to contribute to the community. This project encapsulates the exploration of smart city technology's feasibility and acceptance in less urbanized areas, highlighting the importance of bridging the digital divide to ensure equitable development across diverse regions.

Abstract
This paper presents a case study to illustrate the application of the directed qualitative content analysis (DQCA) technique to focus group transcriptions for data-driven qualitative persona creation, with broader applicability in human-computer interaction and software development. Using a case study from a project focused on creating an e-grocery marketplace for facilitating short agrifood supply chain trade in the Portuguese context, we demonstrate and validate how DQCA can systematically generate personas that reflect real user needs. For the focus group session, we involved one of the project's stakeholders: family farmers. Furthermore, we propose how these personas can be integrated into the Rational Unified Process software development methodology, guiding decision-making, user-centered design, and prioritization throughout all its phases. Despite being rooted in the e-grocery domain, this paper's methodological approach and insights into generating and integrating user-centered personas in software development processes apply to a broader range of industries and projects, offering guidelines for practitioners and researchers in diverse contexts.