Abstract
Reliable black start capability is a critical design requirement for offshore wind-hydrogen energy islands, directly influencing system availability, asset utilization, and the levelized cost of hydrogen production. This paper investi gates black start restoration strategies for autonomous offshore wind-to-hydrogen systems, focusing on the role of grid-forming converter technologies in enabling system recovery following total shutdown. A comparative analy sis of grid-forming battery storage and grid-forming wind turbine generation is conducted using electromagnetic transient simulations of a 300MW offshore wind farm coupled with a 240MW electrolyzer plant. Both technologies are evaluated within a combined soft and hard energization framework incorporating controlled voltage ramping, switchable reactive compensation, and sequential feeder energization. Battery-based grid-forming achieves faster voltage restoration and higher short-term overload capability, while wind turbine-based grid-forming provides superior frequency damping through higher virtual inertia. The combined energization strategy significantly re duces converter sizing requirements compared to pure soft energization, while switchable reactive compensation reduces reactive power burden by 94 percent during multi-feeder restoration. Strategic activation of electrolyzer auxiliary systems provides controllable load management that further attenuates frequency excursions during staged restoration. The findings provide practical design guidelines for black start technology selection in off shore wind-hydrogen systems, with direct implications for converter sizing, capital investment, and hydrogen production continuity.

Abstract
Beyond isolated nodes, subgraphs are fundamental components of networks, with enormous potential to provide a detailed characterization of the underlying systems. Computing subgraph frequencies is therefore a core task indispensable to several other important network metrics. Yet, quantifying these small pieces is computationally challenging, with no known general polynomial-time solution, driving the search for efficient methodologies. Previous counting methods mainly focused on simple classic networks, consisting of a single layer of connectivity. In the last decade, there has been growing interest in multilayer networks, which provide a more realistic representation of complex systems by incorporating layers. With this integration, fundamental concepts were meanwhile presented in a consolidated manner, forming the basis for the development of our proposed strategy. Seeking to contribute to the advancement of multilayer network analysis, but considering the scarcity of available solutions, we focus on a specific and more limited problem before progressing to the more general case. Here, we focus precisely on providing a subgraph counting methodology by computing the frequency of all subgraphs of a specified size in the multiplex case, which is the most widely used type of multilayer networks. Our MPCount implementation builds on the FaSE algorithm, extending it to accommodate multiplex networks by adapting its efficient enumeration and isomorphism identification process to address the introduced layers. This adaptation preserves accuracy, making it an available practical tool for counting subgraphs in multiplex networks, with proof of its performance, supported by experimental results that validate its effectiveness in both synthetic and real world datasets.

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.