Abstract
This study investigates metropolitan-level initiatives in the Porto Metropolitan Area, located in northern Portugal, aimed at fostering healthy and sustainable communities. It maps 41 ongoing municipal projects and analyzes their key thematic areas. In addition to identifying focus areas, the study explores the governance models behind these initiatives, with particular attention to citizen participation and intersectoral coordination. It also examines how local authorities mobilize resources and implement policies, while assessing the integration of Sustainable Development Goals 3 and 11 (SDGs 3 and 11). Employing a qualitative approach through a thematic analysis, and using a comparative framework analysis, this study identifies strong thematic convergence around civic participation, social inclusion, well-being, and environmental sustainability. These elements emerge as key pillars of sustainable development, supporting systemic action across all levels. Citizen participation proves to be a strategic governance tool, requiring structured mechanisms like participatory budgeting. The study also highlights the role of stakeholders, especially schools and companies, in scaling good practices and stresses inclusive policies to reach vulnerable groups. It concludes by advocating for collaborative local governance capable of effectively coordinating diverse actors and promoting equitable, sustainable outcomes.

Abstract
The concept of data spaces has emerged as a structured, scalable solution to streamline and harmonize data sharing across established ecosystems. Simultaneously, the rise of AI services enhances the extraction of predictive insights, operational efficiency, and decision-making. Despite the potential of combining these two advancements, integration remains challenging: data spaces technology is still developing, and AI services require further refinement in areas like ML workflow orchestration and energy-efficient ML algorithms. In this paper, we introduce an integrated architectural framework, developed under the Green.Dat.AI project, that unifies the strengths of data spaces and AI to enable efficient, collaborative data sharing across sectors. A practical application is illustrated through a smart farming use case, showcasing how AI services within a data space can advance sustainable agricultural innovation. Integrating data spaces with AI services thus maximizes the value of decentralized data while enhancing efficiency through a powerful combination of data and AI capabilities.

Abstract
Purpose - Customer-provider relationships unfold through multiple touchpoints across different channels. However, some touchpoints are more important than others. Such important touchpoints are viewed as moments of truth (MOTs). This study examines the impact of a series of touchpoints on an MOT, and the role MOTs play in determining future profitability and other behavioral outcomes (e.g. customer retention and customer cross-buy) in a business-to-business (B2B) context. Design/methodology/approach - Building upon social exchange theory, a conceptual model is proposed and tested that examines the impact of human, digital, and physical touchpoints and past MOTs on customer evaluation of a current MOT and on future customer outcomes. This research employs a longitudinal methodology based on a unique panel dataset of 2,970 B2B customers. Findings - Study results show that all touchpoints significantly contribute to MOTs, while human and physical touchpoints maintain their primacy during MOTs. The impact of MOTs on future customer outcomes is also demonstrated. Practical implications - This study highlights the need for prioritizing human and physical touchpoints in managing MOTs, and for carefully managing MOTs across time. Originality/value - Given its B2B outlook and longitudinal approach, this research contributes to the multichannel and interactive marketing literature by determining relevant touchpoints for B2B customers.

Abstract
As organizations increasingly transition from monolithic systems to microservices, they aim to achieve higher availability, automatic scaling, simplified infrastructure management, enhanced collaboration, and streamlined deployments. However, this migration process remains largely manual and labour-intensive. While existing literature offers various strategies for decomposing monoliths, these approaches primarily focus on architecture-level guidance, often overlooking the code-level challenges and dependencies that developers must address during the migration. This article introduces a catalogue of seven refactorings specifically designed to support the transition to a microservices architecture with a focus on handling dependencies. The catalogue provides developers with a systematic guide that consolidates refactorings identified in the literature and addresses the critical gap in systematizing the process at the code level. By offering a structured, step-by-step approach, this work simplifies the migration process and lays the groundwork for its potential automation, empowering developers to implement these changes efficiently and effectively. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.

Abstract
Background: Virtual Reality (VR) is making education more engaging and accessible, especially for students with Autism Spectrum Disorders (ASD), promoting inclusion and the development of STEM skills in innovative ways. The literature still reveals a significant gap in terms of appropriate educational resources adapted to the specific needs of these students, resulting in difficulties in their inclusion. With the growing need for inclusive approaches in education, it is essential to find solutions to support these students. The aim of this study is to validate the data collection methodology that will enable the development of Virtual Learning Environments with STEM content for students with ASD. Methods: The Design Science Research (DSR) methodology was used to develop a VR artefact for students with ASD. In addition, the Delphi method was applied in the expert involvement phase, which will contribute to the validation of the artefact's specific requirements. Both will allow for an inclusive and distinctive approach to the development of an artefact, with the aim of offering an innovative educational experience, meeting the varied needs and learning styles of students with ASD, optimising the effectiveness of the proposed VLE. Results: The results show a strong acceptance among experts, highlighting the potential positive impact of this approach, although there are aspects to be improved to ensure a more comprehensive and effective approach. Conclusions: This study highlights the successful validation of an innovative virtual reality programme for students with ASD, highlighting the importance of interdisciplinary collaboration and the strong contribution to the advancement of inclusive education.

Abstract
The increasing complexity of wireless technologies, such as Wi-Fi, presents significant challenges for Rate Adaptation (RA) due to the large configuration space of transmission parameters. While extensive research has been conducted on RA for low-mobility networks, existing solutions fail to adapt in Flying Networks (FNs), where high mobility and dynamic wireless conditions introduce additional uncertainty. We propose Linear Upper Confidence Bound for RA (LinRA), a novel Contextual Bandit-based approach that leverages real-time link context to optimize transmission rates in predictable FNs, where future trajectories are known. Simulation results demonstrate that LinRA converges $\mathbf {5.2\times }$ faster than benchmarks and improves throughput by 80% in Non Line-of-Sight conditions, matching the performance of ideal algorithms.