Abstract
Data leakage is a critical issue in deep learning inflating performance and compromise validity, especially in sensitive areas like medical imaging. This study systematically evaluates two common leakage types in oral squamous cell carcinoma classification from histopathology images: (1) preprocessing leakage (global normalization before dataset splitting) and (2) a severe sample-related (patient-related) contamination scenario created by mixing closely related original and augmented images across splits. We trained 11 CNN and Transformer-based models on a public oral cancer histopathology dataset, benchmarking results against published leakage-free baselines. The results obtained show that the configuration with random splitting of original and augmented images (Scenario 2) artificially increased accuracy by up to 18% (mean +14.3%) compared to leakage-free conditions, while the preprocessing-based leakage (Scenario 1) showed smaller deviations (+1.8%). These inflated metrics arise from a combination of cross-split contamination between closely related samples and increased dataset redundancy, rather than genuine gains in generalization ability. Transformers improved leak-free accuracy (+3.9%) but degraded performance in Scenario 2 (-1.4%), revealing sensitivity to sample-specific biases. The observed performance gains under data leakage conditions are methodological artifacts that undermine clinical reliability, with a severe sample-related contamination scenario (Scenario 2) with random splitting of original and augmented images being particularly detrimental due to its promotion of non-generalizable feature learning. The quantitative benchmarks established here-including a mean accuracy gap of 12.5% (Scenario 2 vs. Scenario 1) across 11 models and Transformer architectures’ sensitivity to contamination-reveal fundamental tradeoffs between metric inflation and model trustworthiness. These findings establish quantitative benchmarks for leakage impacts in medical imaging and inform future guidelines for trustworthy AI development in pathology. © The Author(s) 2026.

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
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.

Abstract

Abstract
Hydrogen is becoming a key energy carrier in the transition toward decarbonization, as electrolysis creates strong interdependencies between electricity and hydrogen markets. Accurately representing strategic behaviour in these coupled markets is essential, yet current models fail to capture price-responsive bidding. To address this, a joint hybrid Cournot-Linear Supply Function Equilibria (CLSFE) model is developed and reformulated as an equivalent optimization problem, enabling tractable large-scale analysis. The model is applied to the Iberian system for 2030 and compared with perfect competition and Cournot benchmarks. Results show that hydrogen prices are lowest under CLSFE, with a reduction of about 44% relative to perfect competition and 10% to Cournot, while hydrogen demand increases by up to 58%. Electrolytic hydrogen production rises up to 92%, displacing grey hydrogen and reducing hydrogen-sector emissions. However, renewable self-curtailment reaches 82 TWh, indicating increased market power. These results highlight cross-sector trade-offs and support market design and policy analysis.