Abstract
Energy communities (ECs) are consolidating as strategic instruments for the decentralised integration of renewable energy sources and citizen participation, although their implementation remains uneven. This paper, developed within the framework of the EU-funded ECHO project, assesses the Portuguese EC ecosystem through desk research and a structured questionnaire. The findings identified grid connection constraints, fragmented licensing procedures and weak administrative coordination as principal barriers. Digitisation, flexibility and community-scale storage are recognised as priorities; however, deployment is constrained by an implementation gap between regulation and practice, incomplete technical and regulatory framework, and high upfront costs. Financial vulnerability associated with dependence on subsidies, as well as social barriers related to energy poverty, limited awareness and low participation, further restrict scale-up. The results indicate the need for administrative simplification, stronger institutional coordination and targeted capacitybuilding to support replicable and inclusive EC deployment.

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
PurposeWithin the sports industry, fan tokens have appeared as an instrument for fostering fan engagement and empowering them to participate in club-related decision-making processes via voting mechanisms (e.g. voting rights in decisions like the design of the equipment or the bus, and the ability to win rewards such as VIP experiences with the players).Design/methodology/approachDrawing on the technology acceptance model and consumer behaviour theories, a conceptual model was established to analyse the relationship of perceived ease of use, usefulness, and financial value, brand love, brand engagement, trust, social influence and its influence on the intention to purchase fan tokens. This study also analyses the generation of positive electronic word-of-mouth (e-WOM) derived from the intention to purchase fan tokens. To achieve the proposed objective, a quantitative study was conducted, collecting data through an online survey from football fans with an interest in fan tokens. The conceptual model was analysed using partial least squares (PLS) structural equation modelling (SEM).FindingsResults show that the financial perceived value was the dimension that most contributed to explaining the purchase intention. It was also found that perceived ease of use, trust in fan tokens and brand engagement positively influence the intention to buy fan tokens. Furthermore, findings demonstrate that purchase intention positively drives e-WOM, highlighting that fans' behavioural intentions extend beyond adoption to active digital advocacy.Originality/valueResults offer effective insights for both researchers and sports practitioners, while also presenting an original conceptual model applicable to diverse sports contexts and crypto assets-related research.

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.

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