Abstract
In the context of the diversity and complexity of laboratory processes, it is crucial to address the vulnerabilities associated with healthcare. Proper risk management becomes essential to ensure quality and safety in this environment. In this sense, the application of risk management tools and methodologies plays a crucial role in the identification, assessment and mitigation of potential risks present in laboratory processes performed, especially in a hospital environment. The present work addresses the theme of risk and safety management in a hospital environment, with the aim of promoting a safe environment for this community. The Healthcare Failure Mode and Effect Analysis methodology was applied to identify and mitigate the risks associated with medical equipment used in a medical genetics laboratory. The methodology included data collection, failure analysis, risk quantification, decision tree application and risk evaluation. Among the 19 failures analyzed none demonstrated a Risk Priority Number (RPN) greater than 8, suggesting that the equipment operates within acceptable risk thresholds. The results highlighted the importance of the safety of healthcare professionals and the proper functioning of equipment to ensure patient safety. The study contributed to the development of preventive and corrective actions, as well as providing future improvements and implementation of the methodology in other services of the hospital. © 2025 Elsevier B.V., All rights reserved.

Abstract

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.