Abstract
Innovation management encompasses a broad and complex organisational process that involves identifying and selecting new opportunities, implementing ideas, and capturing value from resulting innovations. The initial phase of this process, the Front End of Innovation (FEI), requires structured procedures to mitigate potential negative impacts across the innovation management chain. Research indicates that effective FEI activities correlate with improved innovation outcomes and a higher likelihood of successful innovation development. Despite its critical importance and the substantial technological demands of the military sector, the application of the FEI approach in defence contexts remains underexplored in academic literature, particularly within the unique circumstances of developing countries. This study employs the iterative design science research methodology to develop the InovaDefesa Ontology, a formal knowledge representation of the FEI phase, specifically tailored to address the challenges of the defence sector in developing economies. The artefact was evaluated through expert interviews, focus groups, and attribute agreement analysis. The proposed domain ontology offers a significant theoretical contribution by adapting and contextualising innovation management models within the military domain, thereby enhancing communication and coordination among stakeholders. On a practical level, it provides actionable insights and recommendations for public policies aimed at strengthening national innovation systems, building technological capacity, and fostering technological independence. These efforts are critical to achieving national sovereignty and advancing sustainable development in developing countries.

Abstract
Software architecture is reflected across multiple artifacts, making it difficult to communicate without proper documentation, which often becomes outdated or unreliable. We propose an approach to support Living Documentation by generating architectural diagrams from Docker Compose files. We implement our approach as a prototype tool that we name Infragenie and conduct an empirical study to show the viability of the approach. The study involved sending questionnaires to maintainers of 378 GitHub repositories. We received 36 responses. Infragenie-generated diagrams were rated as better or much better for most of the 12 projects with previous diagrams. Over 70% of the respondents agreed that our approach improved documentation completeness, consistency, and accessibility, and more than 90% recognized its effectiveness in capturing key architectural elements. We conclude that by using Docker Compose files we were able to provide useful architectural diagrams.

Abstract
Long-term hourly load forecasting (LTLF) is essential for strategic power system planning, yet improvements are often pursued through increasing model complexity rather than enhancing structural representation. This study demonstrates that carefully designed feature engineering-explicitly incorporating calendar decomposition, special-day identification, and climatic-year substitution-substantially improves forecasting accuracy across five European countries. By restructuring the input representation of annual demand into normalized hourly profiles driven by calendar and climatic factors, the proposed framework achieves an average MAPE reduction of approximately 25% relative to baseline formulations, consistently across all case studies. Multiple machine learning models are evaluated (MLR, GRNN, ANN, GBT, LSTM, CNN, SVR, DNN), with GRNN providing the best overall trade-off between accuracy and robustness (average MAPE of 2.77% for the test year). A climatic substitution analysis further shows that inter-annual weather variability induces an intrinsic dispersion that effectively defines a practical performance ceiling for deterministic LTLF models. The results indicate that structured feature representation exerts a stronger influence on performance than incremental increases in algorithmic complexity. The proposed framework offers an interpretable and computationally efficient approach for generating long-term hourly load scenarios under climatic uncertainty.

Abstract
Federated Graph Neural Networks (FedGNNs) have emerged as a promising paradigm for decentralized graph learning across distributed data silos. However, the influence of underlying communication topologies on model accuracy and efficiency remains underexplored. This study presents a topology-aware benchmarking framework for federated GNNs, systematically evaluating the impact of network structure and aggregation strategy on performance and communication overhead. The proposed framework functions as a synthetic, communication-level digital twin that emulates Federated Learning interactions and topology-dependent dynamics under controlled conditions. Four learning schemes-Centralized, Local, FedAvg, and FedAvg-Fedadam-were assessed across three representative topologies: Barab & aacute;si-Albert (BA), Watts-Strogatz (WS), and Erd & odblac;s-R & eacute;nyi (ER). Results demonstrate that centralized training achieved the highest mean ROC-AUC (0.63), while FedAvg-Fedadam attained the best F1-score (0.038), balancing local adaptation and global convergence. Among topologies, BA and WS yielded higher average AUC values (approximately 0.57 and 0.56, respectively) than ER (approximately 0.39). Communication analysis revealed FedAvg as the most efficient strategy, requiring only approximately 3.8 x 105 bytes cumulatively. These findings highlight key trade-offs between accuracy, robustness, and communication efficiency in federated graph learning and provide empirical guidance for topology-aware optimization of distributed GNNs. While the experiments rely on representative synthetic topologies, the insights offer indicative relevance and potential applicability to Internet-of-Things (IoT), vehicular, and cyber-physical networks, where communication structure and bandwidth constraints critically influence collaborative intelligence. By modeling canonical connectivity patterns and releasing our code and data, the proposed benchmarking framework offers a reproducible basis for comparing emerging federated graph architectures under constrained communication conditions.

Abstract

Abstract
Sleep disorders exhibit substantial heterogeneity, and traditional classifications may not fully capture clinically relevant subtypes. Clustering techniques can identify patient subgroups that improve phenotypic characterization and may support personalized management. This systematic review evaluated the application of clustering in sleep medicine, with particular focus on its potential use as a pre-test triage tool prior to formal sleep testing. PubMed/MEDLINE, Embase, Web of Science, and Scopus were searched to February 2025. Eligible studies applied clustering to classify sleep disorders in adults. Two reviewers independently conducted screening, data extraction, and risk-of-bias assessment using QUADAS-2. The protocol was registered on PROSPERO. Fifty-one studies (1983-2025) were included, predominantly focused on obstructive sleep apnea (OSA) (n = 38, 74%). Hierarchical clustering (n = 20) and K-means clustering (n =14) were the most frequently used techniques. Internal validation was reported in only 18% of studies, and external validation was reported in only 1 study. Seven studies relied exclusively on baseline clinical, demographic, or questionnaire data, representing pre-test scenarios, whereas most incorporated polysomnography-derived variables, limiting their applicability to early clinical stratification. Hierarchical clustering was the most commonly applied method; however, the overall lack of validation limits confidence in the robustness and clinical applicability of identified phenotypes. The potential role of clustering as a pre-test triage strategy remains largely unexplored, as most studies focused on post-diagnostic phenotyping and were affected by incorporation bias. Future research should prioritize pre-test clinical variables, rigorously validate internally and externally, and adopt standardized methodological and reporting practices to facilitate clinical translation.