Abstract

Abstract
K-12 digital learning is increasingly shaped by emerging technologies layered onto existing digital infrastructures. In practice, the technologies that dominate attention, especially generative and assistive AI, arrive bundled with new assessment tensions, data flows, acquisition constraints, and inequities in access and support. This article proposes a practitioner-oriented framework for understanding emerging technologies as sociotechnical-immersive systems rather than standalone tools. The framework connects three lenses: (1) a macro sociotechnical circle that foregrounds policy, markets, equity, and governance; (2) a meso environment-design circle that analyzes how learning experiences are configured through system, narrative, and agency; and (3) a micro educational-approaches circle that focuses on the instructional activities educators enact within those environments, using the Immersive Learning Brain (ILB) as a map of practice and strategies. We developed this framework through practitioner sensemaking grounded in practitioner focus group data and aligned it with recent research syntheses on emerging technologies. We illustrate the framework through one worked example and two comparative mini-cases. We conclude with an agenda for researchers and practitioners focused on assessment, equitable infrastructure and support, data stewardship, and environment-design descriptions that move beyond technocentric labels.

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
Feed whose composition matches nutritional requirements of farmed animals is fundamental for sustainable food production. Selective breeding of fish changes growth, body composition and feed utilisation of fish and hence also nutritional requirements. We derived equations that directly estimate the required changes in feed formulation from measured trait changes, bypassing the traditional estimation by experimental dose-response trials. We applied the model to determine changes in optimal feed phosphorus (P) levels in rainbow trout that has been improved across decades by a breeding programme. The equations show that the effect of changes in body composition (body P%, fillet%, viscera%) on digestible phosphorus inclusion level is modest and linear. The effects of FCR (feed conversion ratio) and phosphorus retention efficiency are strong and highly non-linear, implying that the available phosphorus levels for rainbow trout need to be increased due to their improved FCR, and less so due to their increased fillet% and reduced viscera%. Despite the complex non-linear relationships on the observed raw scale (i.e., additive effects), the multiplicative effects of the traits on a log-log scale are actually simple and intuitive. During the last decades, dietary phosphorus has been reduced to limit nutritional loading to environment. Forecasting showed that the required feed phosphorus levels for rainbow trout under selective breeding are expected to be further increased, and a care must be taken not to reduce the dietary levels too much. The equations integrate quantitative genetics and fish nutrition under one united predictive framework, and they are applicable to other fish species.

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.