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across multiple artifacts, making it difficult to communicate without proper documentation, which often becomes outdated or unreliable.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
Observability helps ensure the reliability and maintainability of cloud-native applications. As software architectures become increasingly distributed and subject to change, it becomes a greater challenge to diagnose system issues effectively, often having to deal with fragmented observability and more difficult root cause analysis. This paper builds upon our previous work and introduces three design patterns that address key challenges in monitoring cloud-native applications. Distributed Tracing improves visibility into request flows across services, aiding in latency analysis and root cause detection, Application Metrics provides a structured approach to instrumenting applications with meaningful performance indicators, enabling real-time monitoring and anomaly detection, and Infrastructure Metrics focuses on monitoring the environment in which the system is operated, helping teams assess resource utilization, scalability, and operational health. These patterns are derived from industry practices and observability frameworks and aim to offer guidance for software practitioners. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.

Abstract
Higher education courses teaching about agile software development (ASD) have increased in commonality as the ideas behind the Agile Manifesto became more commonplace in the industry. However, a lot of the literature on how ASD is applied in the classroom does not provide much actionable advice, focusing on frameworks or even moving beyond the software development area into teaching in an agile way. We, therefore, showcase early work on a pattern language that focuses on teaching ASD practices to university students, which stems from our own experiences as educators in higher education contexts. We present five patterns, specifically focused on team and project setup phase: Capping Team Size, Smaller Project Scope, Business Non-Critical Project, Self-assembling Teams, and Team Chooses Topic as a starting point for developing the overall pattern language. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.

Abstract
Introduction Immersive virtual reality (iVR) offers a multisensory environment for education, yet the integration of olfaction remains underexplored. This study examined whether incorporating ambient olfactory stimuli into an iVR environment enhances foreign language vocabulary retention and the user's sense of presence.Methods A between-subjects experiment was conducted with 59 participants who learned German vocabulary in a virtual airport scenario. Participants were assigned to one of five ambient olfactory conditions systematically selected to represent distinct quadrants of the circumplex model of affect: no scent (control), spearmint (pleasant-arousing), lavender (pleasant-calming), burning wood (unpleasant-arousing), or sewage (unpleasant-calming). Vocabulary retention was measured using matching pre- and post-tests, while subjective presence was assessed using the standardised Igroup Presence Questionnaire (IPQp).Results The results indicated that ambient olfactory stimulation, regardless of affective valence or arousal level, did not significantly improve immediate vocabulary retention compared to the control condition. However, scent did impact the subjective experience of presence; notably, an unpleasant, high-arousal scent (burning wood) served as a distraction, significantly reducing perceived spatial presence.Discussion These findings establish an important boundary condition for multisensory educational VR. They demonstrate that the simple addition of ambient, affective scents as a background stimulus is insufficient to drive immediate cognitive learning gains, and may even detract from immersion if unpleasant. Multisensory iVR design must be guided by pedagogical priorities rather than novelty alone, suggesting that relying solely on ambient emotional modulation via olfaction is not a viable strategy for complex cognitive tasks.

Abstract
This study proposes a comprehensive framework integrating eXplainable Artificial Intelligence (XAI) techniques with clustering-based context extraction to enhance energy consumption forecasting in modern office buildings. By leveraging explanation vectors derived from state-of-the-art XAI methods such as SHAP and LIME, our framework identifies latent operational contexts from sensor data aggregated at 15-min intervals. These contexts enable the tailoring of predictive models through feature augmentation, context-specific training, and transfer learning strategies, thereby improving forecasting accuracy compared to conventional approaches. To identify the best-performing models for each context, hyperparameter optimization via grid search is employed across multiple algorithmsincluding Gradient Boosting, Random Forest, and K-Nearest Neighbors. Extensive experiments demonstrate that context-aware models significantly outperform baseline methods, achieving up to a 7% improvement in the coefficient of determination (R-2) and a marked reduction in error metrics. Our findings underscore the importance of integrating XAI with data-driven modeling to enhance predictive performance and model interpretability, which are critical for practical energy management and decision-making in complex building environments.

Abstract
Renewable Energy Communities (RECs) hold potential for enhancing local energy flexibility and supporting a just energy transition. Yet most operate without intelligent coordination, limiting technical performance and raising concerns over fairness and the distribution of benefits. This study examines both the performance and equity dimensions of RECs by combining a critical review of technical, regulatory, and social barriers with simulation-based analysis of a real-world demonstrator developed in the EU-funded OPEVA project. Using real consumption and generation data, we model baseline, rule-based, and intelligent coordination scenarios, as well as expansion cases that integrate additional batteries or EV chargers into underserved households, setting to answer these two questions: Who benefits most from current and future deployments of flexibility technologies? And how can REC systems be expanded to not only aggregate performance gains but also equitable and fair outcomes for all participants? Results show that intelligent control reduces community-level peak demand, ramping, and energy costs while improving renewable self-consumption. However, these benefits are unevenly distributed, concentrated among participants already equipped with flexible assets or with higher demand. Expansion scenarios improve both technical performance and fairness, but inequities persist without deliberate policy intervention. We conclude with open challenges and propose policy and technical measures to ensure that RECs deliver not only efficiency gains but also just and inclusive outcomes.