Abstract

Abstract
This study examines how leading skatewear brands construct symbolic meaning and digital identity on Instagram by analyzing content typologies, message structures, textual features and engagement patterns. A quantitative content analysis was conducted on one hundred and twenty posts from six major brands (Vans, Nike SB, Stussy, Element, Dickies and Carhartt WIP), selected according to digital relevance and recency. Data collection included interaction metrics, publication formats, caption use and specific textual elements, enabling the identification of systematic visual and textual communication patterns. A structured coding grid was applied to classify all variables under analysis. The study contributes to understanding the processes through which brands translate cultural values into symbolic digital narratives, demonstrating how Instagram operates as a primary environment for meaning making, identity signaling and community formation within the urban fashion ecosystem. Results confirm the centrality of visual composition, cultural references and textual economy in shaping symbolic coherence and perceived authenticity. Two dominant communicational profiles emerge: cultural and aspirational brands that rely on emotionally driven narratives associated with art, music and public figures, and pragmatic or institutional brands that adopt functional and objective messaging. Variations in the use of emojis, hashtags and captions indicate differentiated strategies for visibility enhancement and audience engagement. Overall, the findings advance the literature on digital branding, symbolic identity construction and communicational meaning making within social media environments applied to skatewear brands.

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
This study aims to contribute to a better understanding of the current state of Primary Health Care, specifically in terms of efficiency and user satisfaction at 8 Health Centers in Praia City, Cape Verde, observed in 2017, 2018, 2020 and 2021. The Data Envelopment Analysis (DEA) model with input orientation and Constant Returns of Scale (CRS) is used to measure operational efficiency, and the income to expenditure ratio is used to measure economic and financial efficiency. User satisfaction is assessed using a questionnaire survey involving 408 respondents. The results indicate that 8 Decision-Making Units (DMUs) are operationally efficient. Regarding economic and financial efficiency, the ratios observed were generally higher than 1 in the different Health Centers analyzed. The satisfaction results show that most users are satisfied with the organization and services provided by the Health Centers. However, some improvements are needed in all the categories evaluated. A positive association between operational efficiency and the economic and financial efficiency ratio stands out as a result of comparing the different types of efficiency. Operational efficiency shows a negative relationship with the dimensions of satisfaction related to organization, service process, medical and nursing services, but this relationship is only statistically significant for the service process.

Abstract
Fishery analysis is critical for ensuring the sustainability of marine species and supporting the livelihoods of millions who rely on fishing for food and income. Commercial catch analysis provides essential insights for stock assessments and management by estimating fishing effort and its ecological impacts. Accurate fish size data is vital for fish stock assessment modeling and to understand fishing impacts, but manual size sampling and annotation of daily landings are impractical and error-prone. In this paper, we leverage a unique data set of fish images and measurements from 2022 to 2024 to solve fish length distribution sampling and prediction tasks. We demonstrate that extracting key information from images can enhance predictive performance, often surpassing more complex methods. Furthermore, species-specific segmentation models outperformed those trained on mixed classes, underscoring the importance of tailored segmentation in achieving accurate predictions. This work highlights the potential of automated systems to improve fish population stock assessments, a critical and complex challenge in management and conservation efforts.

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.