Abstract

Abstract
When shopping for perishable products, consumers typically prefer the freshest items, especially those with a short shelf life. With that in mind, retailers establish strict contractual agreements with suppliers to ensure the fulfilment of their orders for perishable products. One key condition in these agreements is the Minimum Life On Receipt (MLOR) rule, which defines the maximum product age that the retailer will accept at full price. In this study, we propose a model that facilitates the negotiation of retailer–supplier terms to increase flexibility. Specifically, we define the share of orders retailers should accept beyond the MLOR at a discounted price. We formulate the problem as a bilevel program considering the individual objectives of the retailer (leader) and the supplier (follower), while also accounting for consumer demand driven by both price and product freshness. To address the bilevel problem, we employ a reformulation-and-decomposition algorithm adapted from the literature. We then compare the supply chain benefits of solving the bilevel program with those of optimising the retailer’s and supplier’s objectives jointly in a centralised approach, as well as to standard contract terms in which products are returned if the supplier does not meet the MLOR requirement. Our results demonstrate that flexible agreements offer significant benefits, with average profit increases of up to 4% for retailers and up to 13% for suppliers. Finally, we provide suggestions for designing new clauses that account for consumer demand variability and retailer’s order frequency.

Abstract
Virtual Reality (VR) applications hold significant promise for enhancing healthcare training by providing immersive and interactive environments for skills development. This scoping review analyzed the methods, and instruments used for usability assessment in VR applications designed to support healthcare training. An electronic database search identified 19 studies meeting specific inclusion and exclusion criteria. The included studies focused on two primary objectives: (i) usability assessment, and (ii) usability and feasibility assessment, evaluating not only the usability but also the practicality and sustainability of VR applications in healthcare training settings. The findings reveal inconsistencies in the reporting of methodological details essential for robust usability assessment, particularly in terms of methods' triangulation and participants' sample sizes. This review highlights the need for more rigorous and comprehensive approaches that combine both test and inquiry methods to ensure that VR applications present good usability, which is impactful for sustainable healthcare training applications.

Abstract
The increasing complexity of modern power systems, driven by renewable energy integration, distributed energy resources, and evolving operational requirements, has intensified the need for advanced optimization tools capable of ensuring secure and economical system operation. Security-Constrained Optimal Power Flow (SCOPF) is a fundamental tool used to ensure system security under N-1 contingency scenarios. This paper proposes a novel nonlinear programming (NLP)-based multi-period SCOPF formulation that enhances computational efficiency and scalability while preserving solution accuracy. The proposed approach explicitly incorporates load shifting, energy storage systems (ESSs), and renewable generation with reactive power injection, addressing the operational needs of modern, renewable-dominated grids. The methodology is validated using IEEE 9-, 30-, 118-, and 300-bus systems, demonstrating consistent scalability across network sizes. Unlike conventional approaches that rely on linearization or DC approximations to reduce computational burden, the proposed NLP framework directly addresses the full nonlinear AC power flow equations. Simulation results indicate that the method effectively handles the non-convexities of the problem, ensuring superior solution accuracy and strict adherence to voltage and reactive power constraints. These results confirm that the proposed framework offers a reliable and robust tool for secure power system operation, capable of managing the complex dynamics of renewable penetration without compromising physical power systems modelling fidelity.

Abstract
Background: This study aimed to validate the content of a game focused on clinical supervision in nursing, with the collaboration of experts, and to assess its usability alongside a group of nurses. The development of SUPERVISE (R) was grounded in theories of Experiential Learning, Self-Determination, Constructivist, and Social Cognitive.Methods: A mixed study design was used. In the first phase, the content of the game was validated with the participation of experts using a modified e-Delphi method. In the second phase, the usability of SUPERVISE (R) was tested with nurses.Results: In the first phase, the content of the game was validated by 36 experts, reaching a consensus = 95.4% on the 128 questions on which the game was based. In the second phase, the SUPERVISE (R) game was tested and evaluated by 39 nurses. It showed good usability and with a System Usability Scale score = 79.4 (above the cut-off of 68) and was recognised as an effective teaching strategy.Conclusion: This study highlights the importance of combining rigorous content validation with practical evaluation to develop effective gamified educational tools for nursing practice.

Abstract
As Artificial Intelligence (AI) systems increasingly permeate sensitive domains such as finance, healthcare, and media, ensuring their ethical deployment has become a central concern for researchers, policymakers, and practitioners. Current auditing tools often assess isolated principles, such as fairness or explainability, lacking a comprehensive view of the ethical risks involved. This paper presents a multidimensional framework for ethical evaluation of AI systems, designed to support responsible AI governance and alignment with the United Nations Sustainable Development Goals (SDGs). The proposed approach enables the simultaneous analysis of key ethical dimensions, including fairness, bias, explainability, robustness, transparency, and legal compliance. We demonstrate the applicability of this tool through one extensive case study: a credit scoring system, considered high-risk under the AI Act. This work contributes to operationalizing responsible AI governance, providing insight for policymakers, regulators, and practitioners to ensure ethical, legally compliant, and socially responsible AI deployment.