Abstract

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.

Abstract
The entrepreneurial performance of new ventures operating within the sustainable open innovation paradigm remains underexplored, particularly in terms of how specific sustainability-oriented practices translate into measurable performance outcomes. Prior research has largely examined sustainability, entrepreneurship, and open innovation in isolation, offering limited empirical evidence on their combined effects at the early venture stage. To address this gap, this study analyzes panel data from 407 new ventures incubated in science and technology parks, employing regression-based panel data analysis to examine the relationships between sustainable practices, open innovation engagement, and entrepreneurial performance. The findings suggest that new ventures widely adopt sustainable materials and energy as key strategies, which significantly influence entrepreneurial performance. In contrast, support from local communities does not have a statistically significant impact. Among the sociodemographic factors tested, only the number of years participating in open innovation networks shows a significant effect on entrepreneurial performance. Theoretically, this study advances sustainable open innovation literature by empirically integrating sustainability practices into entrepreneurship performance models. From a managerial perspective, the findings offer actionable insights for entrepreneurs and incubator managers, highlighting which sustainability strategies and network engagements are most likely to yield performance benefits in new ventures.

Abstract
Early fault detection remains a critical challenge in predictive maintenance (PdM), particularly within critical infrastructure, where undetected failures or delayed interventions can compromise safety and disrupt operations. Traditional anomaly detection methods are typically reactive, relying on real-time sensor data to identify deviations as they occur. This reactive nature often provides insufficient lead time for effective maintenance planning. To address this limitation, we propose a novel two-stage early detection framework that integrates time series forecasting with anomaly detection to anticipate equipment failures several hours in advance. In the first stage, future sensor signal values are predicted using forecasting models; in the second, conventional anomaly detection algorithms are applied directly to the forecasted data. By shifting from real-time to anticipatory detection, the framework aims to deliver actionable early warnings, enabling timely and preventive maintenance. We validate this approach through a case study focused on metro train systems, an environment where early fault detection is crucial for minimizing service disruptions, optimizing maintenance schedules, and ensuring passenger safety. The framework is evaluated across three forecast horizons (1, 3, and 6 hours ahead) using twelve state-of-the-art anomaly detection algorithms from diverse methodological families. Detection performance is assessed using five performance metrics. Results show that anomaly detection remains highly effective at short to medium horizons, with performance at 1-hour and 3-hour forecasts comparable to that of real-time data. Ensemble and deep learning models exhibit strong robustness to forecast uncertainty, maintaining consistent results with real-time data even at 6-hour forecasts. In contrast, distance- and density-based models suffer substantial degradation at longer horizons (6-hours), reflecting their sensitivity to distributional shifts in predicted signals. Overall, the proposed framework offers a practical and extensible solution for enhancing traditional PdM systems with proactive capabilities. By enabling early anomaly detection on forecasted data, it supports improved decision-making, operational resilience, and maintenance planning in industrial environments.