Abstract
Background: The increasing complexity of musculoskeletal surgical nursing education requires innovative pedagogical strategies that integrate immersive technologies with structured instructional design to enhance clinical reasoning and theory-practice integration. An: To design and develop a nursing process-structured immersive virtual reality simulation for orthopaedic nursing education and to pilot test its usability and educational appraisal among undergraduate nursing students. Design Pilot mixed-methods study. Method: The simulation, Nurse TechGames, was designed as a three-dimensional orthopaedic inpatient scenario structured sequentially according to the nursing process and incorporating gamification elements to support chnical reasoning. The intervention was implemented using Meta Quest 3 head-mounted displays. Usability was assessed using the System Usability Scale, and educational appraisal was measured uring the Serious Educational Glime in Nursing Appraisal Scale. Open-ended responses were analysed through qualitative content analysis. Participants were monitored during and after the sessions for potential cybersickness symptoma Results: The simulation achieved a mean score of 83.15 on the System Usability Scale, indicating excellent us-ability. The total mean SEGINAS score was 95.58, reflecting a very high pedagogical evaluation across the di mensions of engagement, impact on learning, and content relevance. The qualitative analysis identified eight categories, with no reports of significant cyberzickness symptoms: Perceived Learning Value, Clinical Transfer, Realism and Immersion, Engagement and Motivation, Technical Robustness, Development Potential, Minor Technical Issues, and Time Constraints. Conclusion: The inmersive simulation NurseTechGames demonstrated high usability and strong pedagogical allceptance in musculoskeletal surgical nursing education. Future controlled and longitudinal studies are required to evaluate objective and sustained impact on learning outcomes.

Abstract
Cardiovascular diseases remain the leading cause of morbidity and mortality worldwide. Continuous electrocardiographic (ECG) monitoring is essential for prevention and treatment, but conventional approaches based on the need for some voluntary action often limit comfort and adherence in long-term use. This study investigates the feasibility of acquiring ECG signals from a toilet seat interface embedding dry electrodes in the posterior thighs. A total of 30 hospitalised patients with diverse cardiovascular conditions-including arrhythmias, ischemic heart disease, heart failure, structural abnormalities, and aneurysms-were enrolled. Thigh-acquired ECGs were recorded simultaneously with conventional limb-lead signals and analysed for morphology, heart rate variability (HRV), and disease-related clustering. Thigh-based ECGs demonstrated clear P-QRS-T complexes with preserved morphology, allowing reliable extraction of mean templates and HRV metrics. The comparison between pathological and normal groups showed that post-surgical aortic repair patients had ECG profiles closest to the normal cluster; in contrast, aortic stenosis (AS) appeared most distant. HRV analysis revealed disease-specific autonomic patterns: patients with tricuspid or mitral involvement exhibited higher variability (SDNN up to 140 ms), whereas those with aortic valve disease presented markedly reduced parasympathetic indices (RMSSD and pNN50). Principal component analysis of multi-feature ECG data identified overlapping groups of Acute Coronary Syndrome, Unstable Angina and Ascending Aortic Aneurysm. At the same time, hierarchical clustering confirmed the distinct separation of conditions with severe hemodynamic disruption, such as PS and AS. These findings support the feasibility of unobtrusive thigh-based ECG monitoring via a toilet-seat interface, enabling reliable signal acquisition, HRV analysis, and preliminary patient stratification. This approach may lay the groundwork for future home-based cardiovascular screening and telemedicine applications.

Abstract

Abstract
This special issue showcases state-of-the-art research at the intersection of analytics and retail operations. As the retail landscape becomes increasingly complex - driven by omnichannel strategies, evolving customer expectations, and a surge in data availability - analytics has emerged as a critical enabler of operational efficiency, customer experience, responsiveness, and sustainability and ethics. Collectively, these contributions demonstrate how advanced analytics can support retailers in navigating uncertainty, personalizing services, and scaling up innovation across formats and channels. The articles featured in this issue address a diverse set of decision domains, including warehousing, inventory and assortment planning, and distribution and last-mile delivery. Methodologically, they span descriptive, prescriptive, and hybrid approaches, leveraging tools such as machine learning, stochastic modeling, and dynamic optimization. By grounding models in real-world data and focusing on practical implementation, the issue provides actionable insights for both scholars and practitioners. It also highlights emerging opportunities for future research on behavioral integration, human-machine collaboration, and the ethical dimensions of retail analytics.

Abstract
In this paper, we introduce a novel approach for rigorously verifying safety properties of state machine specifications. Our method leverages an auto-active verifier and centers around the use of action functions annotated with contracts. These contracts facilitate inductive invariant checking, ensuring correctness during system execution. Our approach is further supported by the Why3-do library, which extends the Why3 tool's capabilities to verify concurrent and distributed algorithms using state machines. Two distinctive features of Why3-do are: (i) it supports specification refinement through refinement mappings, enabling hierarchical reasoning about distributed algorithms; and (ii) it can be easily extended to make verifying specific classes of systems more convenient. In particular, the library contains models allowing for message-passing algorithms to be described with programmed handlers, assuming different network semantics. A gallery of examples, all verified with Why3 using SMT solvers as proof tools, is also described in the paper. It contains several auto-actively verified concurrent and distributed algorithms, including the Paxos consensus algorithm.

Abstract
Purpose - This study proposes a conceptual framework for strategic sourcing tailored to Project Management Offices (PMOs) operating in complex Research, Development and Innovation (R&D&I) environments, examining how R&D&I PMOs orchestrate sourcing by identifying key elements and practices that define this strategic role. Design/methodology/approach - This study uses a qualitative approach grounded in a three-year ethnographic immersion within a military scientific, technological and innovation institution. Data collection involved participant observation, document analysis and informal interviews, enabling an in-depth examination of sourcing dynamics. Findings - The resulting framework integrates three interdependent pillars: five foundational sourcing dimensions; a strategic make-or-buy decision matrix; and management categories aligned with R&D&I operations. The findings show that PMOs coordinate strategic sourcing by integrating internal and external capabilities, thereby enhancing organizational responsiveness in complex innovation ecosystems. Research limitations/implications - While the single-case ethnographic study focuses on the aerospace and defense sector, the framework distinguishes between general conceptual pillars and context-specific applications, supporting its conceptual transferability to other highly regulated sectors such as healthcare and pharmaceuticals. The study provides actionable guidance for managing technological uncertainty and power dynamics, while addressing economic, political and teaching implications. Practical implications - The proposed framework offers PMO managers a strategic sourcing model suited to complex environments such as the defense sector. It strengthens decision-making by making make-or-buy tradeoffs explicit, documented and comparable across technology acquisition, capability development, outsourcing boundaries and interinstitutional partnerships under confidentiality and intellectual property constraints. The model addresses recurring problems in R&D&I settings, including fragmented criteria, inconsistent rationales and limited traceability, enhancing transparency, governance and alignment with organizational goals. It positions the PMO as a strategic actor in acquisition and technological alliance decisions, offering guidance for institutional adaptation, particularly relevant for public organizations facing budgetary and regulatory constraints. Social implications - The societal implications of this study stem from the role of the R&D&I PMO as a catalyst for technological sovereignty and national development. By structuring strategic sourcing in highly complex environments, the proposed model strengthens the national technological and industrial base, reduces dependence on external critical technologies and enhances innovation capacity. The findings show that dual-use R&D projects generate positive spillovers for industry and academia, fostering regional development and national competitiveness. By coordinating government, industry and research institutions through the Triple Helix, the PMO helps ensure that R&D&I investments translate into tangible socioeconomic benefits for society. Originality/value - This research addresses the underexplored intersection of strategic sourcing, project management and innovation governance. It goes beyond theoretical abstraction by providing a model for navigating technological uncertainty. It explores how emerging digital technologies (such as Artificial Intelligence and blockchain) can refine decision-making and support the automation of Intellectual Property safeguards.