Abstract
The Circular Economy, an alternative to the linear make-use-dispose system, promotes sustainable development through novel business models. Thus, Circular Business Models emerge as systems that minimize resource input and waste by slowing, closing, and narrowing material and energy loops. Circular Startups play a crucial role in the transition to a Circular Economy. Despite their significance, there is a research gap in how these companies scale. Moreover, the slow transition is attributed to the limited scalability of Circular Business Models, which leads to the need to scale current practices. The present study aims to fill this gap by defining a typology of scalability strategies employed by circular startups. A qualitative case studies methodology is adopted, using document analysis and semi-structured interviews conducted in the context of the European project SoTecIn Factory. This research identifies five scalability strategies used by Circular Startups-impact, commercial, ecosystem, institutional and cultural-with the commercial strategy being the main focus in terms of growth approach. The findings underline a strong commitment across the observed value chains to minimize environmental impact, enhance social welfare, and foster economic growth. Other key findings reveal the presence of R-imperatives across different value chains, leading to industry-specific approaches. In addition to the theoretical contribution, this research can support sustainable growth by practitioners in their scaling efforts, thus, accelerating the circular transformation. © 2025 The Authors.

Abstract
The increasing integration of Artificial Intelligence (AI) across various sectors of society raises complex ethical challenges requiring systematic and scalable oversight mechanisms. While tools such as AIF360 and Aequitas address specific dimensions, namely fairness, there remains a lack of comprehensive frameworks capable of auditing multiple ethical principles simultaneously. This paper introduces a multidimensional AI auditing tool designed to evaluate systems across key dimensions: fairness, explainability, robustness, transparency, bias, sustainability, and legal compliance. Unlike existing tools, our framework enables simultaneous assessment of these dimensions, supporting more holistic and accountable AI deployment. We demonstrate the tool’s applicability through use cases and discuss its implications for building trust and aligning AI development with fundamental ethical standards.

Abstract
Agriculture and energy use are increasingly linked, especially as farms' energy needs grow. Renewable Energy Communities (RECs) help farmers, particularly in remote areas, access affordable surplus energy from other producers, while sellers gain extra revenue. This study focuses on the creation of RECs as a sustainable and economically viable solution for small and medium-sized agribusinesses to address their energy challenges. We explore the complementarities and potential benefits of RECs from the experience learned in the Tools4AgriEnergy project, using RECreation digital platform for the management of RECs. A case study is used, based on the Alqueva region in Portugal with six members that develop different agri-food sector activities. Using tariffs compliant with Portuguese regulations, results indicate that the development of self-consumption activities can achieve significant energy cost savings annually.

Abstract
With the recent surge in the electric vehicle market, there is a pressing demand for solutions and platforms to enhance vehicle lifecycle management. This is particularly pertinent for motorcycles, which are widely used in urban environments (e.g., for food delivery services) and require frequent maintenance. The present study proposes the research and development of a platform, along with mobile and web applications, focusing on optimizing the lifecycle of electric motorcycles. Central to this project is the implementation of Product Lifecycle Management (PLM) to simplify the planning of technical maintenance and the recording and access to technical events and information in the most transparent and non-intrusive way for all involved parties. This project aims to establish innovative and effective communication between owners, manufacturers, and service partners, ensuring the longevity and reliability of motorcycles.

Abstract
Underwater environments pose unique challenges to optical systems due to physical phenomena that induce severe data degradation. Current imaging sensors rarely address these effects comprehensively, resulting in the need to integrate complementary information sources. This article presents a multimodal data fusion approach to combine information from diverse sensing modalities into a single dense and accurate tridimensional representation. The proposed fusiNg tExture with apparent motion information for underwater Scene recOnstruction (NESO) encoder-decoder network leverages motion perception principles to extract relative depth cues, fusing them with textured information through an early fusion strategy. Evaluated on the FLSea-Stereo dataset, NESO outperforms state-of-the-art methods by 58.7%. Dense depth maps are achieved using multi-stage skip connections with attention mechanisms that ensure propagation of key features across network levels. This representation is further enhanced by incorporating sparse but millimeter-precise depth measurements from active imaging techniques. A regression-based algorithm maps depth displacements between these heterogeneous point clouds, using the estimated curves to refine the dense NESO prediction. This approach achieves relative errors as low as 0.41% when reconstructing submerged anode structures, accounting for metric improvements of up to 0.1124 m relative to the initial measurements. Validation at the ATLANTIS Coastal Testbed demonstrates the effectiveness of this multimodal fusion approach in obtaining robust tri-dimensional representations in real underwater conditions.

Abstract