Abstract

Abstract

Abstract
Renewable Energy Communities (RECs) are emerging as key enablers of decentralized, sustainable, and consumer-driven energy systems. Beyond environmental benefits, RECs possess significant potential to enhance resilience against extreme weather, price volatility, and infrastructure fragility. This article integrates resilience and relia bility constraints directly into the planning and operation of RECs, assessing their impact on system cost, sizing, and dispatch. Two optimization models are developed: a design model that sizes community assets (PV and BESS) using varying resilience indicators, and an operational model that minimizes costs while monitoring reliability. The analysis introduces two resilience metrics, deterministic hourly autonomy and average autonomy, and eval uates them using real-world data from the Caxias Living Lab. Results demonstrate that average resilience can be increased with minimal cost impacts due to non-linear trade-offs, whereas strict hourly resilience requires signifi cant storage investment. Furthermore, a Value of Lost Load (VoLL) reliability indicator is shown to cost-effectively trigger maintenance events. This framework offers actionable guidance for designing sustainable, adaptive, and economically viable energy communities.

Abstract

Abstract
With the growing popularity of DevOps culture among companies and the corresponding increase in Microservices architecture development-both known to boost productivity and efficiency in software development-an increasing number of organizations are aiming to integrate them. Implementing DevOps culture and best practices can be challenging, but it is increasingly important as software applications become more robust and complex, and performance is considered essential by end users. By following the Design Science Research methodology, this paper proposes an iterative framework that closely follows the recommended DevOps practices, validated with the assistance of expert interviews, for implementing DevOps practices into Microservices architecture software development, while also offering a series of tools that serve as a base guideline for anyone following this framework, in the form of a theoretical use case. Therefore, this paper provides organizations with a guideline for adapting DevOps and offers organizations already using this methodology a framework to potentially enhance their established practices.

Abstract
Deep-learning beat-tracking algorithms have achieved remarkable accuracy in recent years. However, despite these advancements, challenges persist with musical examples featuring complex rhythmic structures, especially given their under-representation in training corpora. Expanding on our prior work, this paper demonstrates how our user-centred beat-tracking methodology effectively handles increasingly demanding musical scenarios. We evaluate its adaptability and robustness through musical pieces that exhibit rhythmic dissonance, while maintaining ease of integration with leading methods through minimal user annotations. The selected musical works-Uruguayan Candombe, Colombian Bambuco, and Steve Reich's Piano Phase-present escalating levels of rhythmic complexity through their respective polyrhythm, polymetre, and polytempo characteristics. These examples not only validate our method's effectiveness but also demonstrate its capability across increasingly challenging scenarios, culminating in the novel application of beat tracking to polytempo contexts. The results show notable improvements in terms of the F-measure, ranging from 2 to 5 times the state-of-the-art performance. The beat annotations used in fine-tuning reduce the correction edit operations from 1.4 to 2.8 times, while reducing the global annotation effort to between 16% and 37% of the baseline approach. Our experiments demonstrate the broad applicability of our human-in-theloop strategy in the domain of Computational Ethnomusicology, confronting the prevalent Music Information Retrieval (MIR) constraints found in non-Western musical scenarios. Beyond beat tracking and computational rhythm analysis, this user-driven adaptation framework suggests wider implications for various MIR technologies, particularly in scenarios where musical signal ambiguity and human subjectivity challenge conventional algorithms.