Abstract
Circular startups (CSUs) play a crucial role in the circular transition by developing circular business models (CBMs) that minimise resource use and narrow material and energy loops. However, empirical research on how CBMs shape growth strategies and how ecosystems enable or constrain scaling remains limited. This study aims to fill this gap by analysing the growth strategy of CSUs, addressing their circularity, business model and scalability strategies. It analysed 44 CSUs operating in packaging and plastics, textiles and food, water and nutrients value chains, using a qualitative multiple-case design. Results show that CSUs predominantly adopt Commercial and ecosystem scalability strategies, linking replication and geographical expansion with access to partners, resources and markets, and implementing platform- or waste-based CBMs. The study expands existing frameworks by conceptualising Ecosystem Strategy as a core scalability approach and clarifying its mechanisms, offering guidance for entrepreneurs and policymakers seeking to foster circular transformation.

Abstract
Companies are increasingly adopting technologies such as Robotic Process Automation (RPA) to reduce costs and improve productivity. RPA is deployed in areas like accounting, payroll, and finance to automate business processes. While RPA does not necessarily result in unemployment, it has notable effects on employees and company governance. This study explores the impact of RPA implementation on employees and company governance, using a qualitative methodology based on thirteen semi-structured interviews with RPA experts from four multinational companies. The results indicate that the impacts of RPA vary depending on the automation strategy adopted (task-oriented or process-oriented). In task-oriented strategies, citizen developers often play a central role, contributing to rapid implementation. In contrast, process-oriented strategies tend to rely on professional developers and require more structured governance. The findings also point out that RPA influences not only task execution but also employee upskilling, job role redefinition, and the evolution of governance models. The study proposes an integrated framework linking automation strategy, governance, upskilling, and employee adaptation, offering both practical insights and theoretical contributions to digital transformation research and for managing risks and enhancing workforce capabilities. It also advances academic understanding by linking real-world RPA implementations to organisational and technological impacts.

Abstract

Abstract
Robotic bin-picking is a critical operation in modern industry, which is characterised by the detection, selection, and placement of items from a disordered and cluttered environment, which can be boundary limited or not, e.g. bins, boxes or containers. In this context, perception systems are employed to localise, detect and estimate grasping points. Despite the considerable progress made, from analytical approaches to recent deep learning methods, challenges still remain. This is evidenced by the growing innovation proposing distinct solutions. This paper aims to review perception methodologies developed since 2009, providing detailed descriptions and discussions of their implementation. Additionally, it presents an extensive study, detailing each work, along with a comprehensive overview of the advancements in bin-picking perception.

Abstract
Manual selection of real estate properties can pose considerable challenges for agents since it needs a careful balance of various factors to satisfy client requirements while also manoeuvring through the complexities of the market. Although automated valuation models are widely used to estimate property market values, they are not designed to support property recommendation tasks. To address this gap, filteringbased recommendation methods have been explored, including collaborative and content-based approaches. However, these methods face several limitations in the real estate domain. This paper proposes a recommendation methodology designed to identify houses that closely resemble a given property, allowing agents to select the best matches based on geographical and physical characteristics. To assess the performance of the proposed methodology, we employ a range of evaluation metrics that measure different aspects of the model's effectiveness in ranking and recommending relevant items. The findings suggest that, while geographic features may slightly influence ranking behaviour, the model is capable of producing diverse and relevant recommendations consistently.

Abstract
The European Union Internal Electricity Market is undergoing major reforms to support the transition to a fully decarbonized energy system by 2050, where non-dispatchable renewable energy sources play a central role. To enhance market efficiency, renewable energy sources integration, and power system balancing, the European Union promotes increased cross-border interconnection and cooperation among Member States. This paper reviews existing literature and market models addressing multi-zone interconnection capacity allocation and proposes a novel inter-zonal co-optimization mechanism for the joint allocation of energy and automatic balancing reserve capacity based on system cost minimization. Unlike previous approaches that treat energy and reserve coordination separately or sequentially, this study introduces a unified optimization framework that captures the interdependencies of intra-and inter-zonal dispatch. The proposed mechanism is implemented within the CEVESA market model and applied to a realistic Iberian case study, assessing its economic and operational impacts under varying interconnection capacity scenarios. Results show that while energy coordination alone achieves significant cost reductions, joint coordination of energy and reserves delivers further efficiency gains, reduces reserve price volatility, and enhances cross-border system flexibility.