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.

Abstract
Automated lung CT segmentation is a prerequisite for quantitative pulmonary analysis. However, the standard 'accuracy-first' development cycle often neglects clinical safety, as traditional metrics like the Dice Similarity Coefficient do not differentiate between benign boundary errors and serious omissions such as missed malignant nodules. This study proposes a human-centric framework to enhance the trustworthiness of lung segmentation by integrating annotation governance, predictive uncertainty quantification, and a risk-based evaluation taxonomy. A ResNet34 + U-Net hybrid model was developed. A specialized annotation harmonization protocol was implemented to ensure the consistent inclusion of juxta-pleural nodules. Predictive uncertainty was quantified via Monte Carlo Dropout to generate voxel-wise predictive entropy maps. The system was evaluated using a proposed risk-based error taxonomy and simulated a human-AI triage workflow. Result obtained showed model M3, which incorporated annotation correction and data augmentation, achieved 0.66% Dice improvement over baseline M1. The framework reduced moderate-risk errors associated with non-lung inclusion by 30% (0.396% ? 0.276%). From the human-AI triage simulation, 37.2%, 16.9% and 10.8% high-risk cases were recalled at operating thresholds (t) of 0.0005,0.003 and 0.01, respectively. Our framework provides a robust foundation for semiautonomous clinical workflows by prioritizing error transparency and risk-aware evaluation. Thus, shifting the paradigm from opaque automation to trustworthy human-AI collaboration. © 2026 IEEE.

Abstract
The rapid expansion of solar photovoltaic (SPV) systems poses critical challenges to material supply security and waste management. Addressing these challenges require integrating circular economy strategies. This study develops a dynamic and probabilistic material flow analysis (MFA) to quantify the lifecycle material flows of crystalline silicon (c-Si) modules from 1998 to 2050, with waste projections extended to 2099. Three circular economy scenarios are evaluated, integrating the European Union Directive targets and strategies for reducing, reusing, and recycling. Uncertainty is explicitly addressed through Monte Carlo simulation, capturing variability in installed capacity projections, Weibull lifetime parameters, material composition, pre-operational losses, and recycling efficiencies. Portugal is used as a national-scale case study to demonstrate the applicability of the proposed methodology. Results indicate a cumulative material requirement of approximately 1.46 Mt by 2050 without circular strategies. Across low-, medium-, and high-circularity scenarios, both total material demand and the share of primary versus secondary raw materials vary substantially. Notably, scenarios incorporating reuse may increase primary material extraction due to reduced availability of secondary materials for manufacturing. Deterministic analysis suggests that full c-Si loop closure can be achieved between 2039 and 2041, depending on the scenario. However, probabilistic results reveal substantial uncertainty, with the probability of 100% Circular Material Use Rate (CMUR) in the period 2030-2050 among 53.7%, 43.6% and 68.6% under low, medium, and high circularity respectively. Sensitivity analysis identifies future c-Si's deployment and lifetimes as the dominant drivers of circularity outcomes. This probabilistic MFA contributes with robust evidence to support circular economy policy design and infrastructure planning while opening avenues for further research.

Abstract
Demand Responsive Transport (DRT) systems provide versatile transport operations and are capable of quickly adjusting to fluctuating passenger demand. Unlike traditional public transport (PT), which operates with fixed routes and schedules, DRTs offer flexibility in vehicle routes, fleet sizes, and schedules. This flexibility is an intrinsic characteristic of DRT systems and a key attribute in their design and associated decision-making processes. However, flexibility also presents significant design challenges, due to the multitude of potential configurations and the unique characteristics of each service area. As practice shows, the effectiveness of DRT configurations is heavily influenced by demand levels. Highly flexible operations are typically suited for low-demand areas, whereas higher demand may require reduced flexibility to maintain system efficiency. Furthermore, demand may grow to a point where the operators may question whether to continue operating as a DRT or shift to traditional regular public transport, with predefined routes and schedules, and more efficient operation. This work studies how demand levels and characteristics can be used in the decision to adopt a DRT system, instead of PT. The problem was addressed through the simulation of various demand scenarios in a virtual environment, thus comparing the performance of the different transport systems. In the scenario analysed, it was possible to identify a demand threshold where the DRT system is more efficient, while higher demand favours the fixed-route system. However, it is important to note that this threshold may be significantly influenced by the specific characteristics of the service area where the system will operate. Copyright © 2025. Published by Elsevier B.V.

Abstract
Ambient intelligence (AmI) refers to environments where smart devices, sensors, and AI-driven systems work seamlessly to enhance human interactions with their surroundings. Through the combination of real-time data, context-awareness, and adaptive learning, AmI enables environments to respond proactively to user needs, improving efficiency, comfort, and decision-making. However, since AmI systems are inherently human-centric and often operate autonomously, they must be designed with robust ethical, privacy, and safety considerations. Ensuring that these systems function reliably, fairly, and without harm is crucial, especially in sensitive domains like healthcare, security, and smart infrastructure. This work introduces a novel tool, conceptualized as an AmI Digital Twin, which allows developers to simulate or monitor AmI data streams, and develop and thoroughly test AmI applications before and during their real use. Built on a modular architecture leveraging technologies like React.js, Node.js, Kafka, Faust, MongoDB, InfluxDB, Grafana, and Docker, the platform ensures adaptability to different application environments, scalability, and ease of deployment. Besides the description of the tool itself, we provide some early validation results in common AmI tasks such as anomaly and concept drift detection. The tool is available in a public repository, and comes pre-packaged with a set of applications for AmI use-cases. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.

Abstract
Rapid Eye Movement (REM) sleep is marked by intense brain activity coupled with muscular atonia. When this mechanism fails, abnormal behaviors may occur, often indicating REM Sleep Behavior Disorder (RBD) and serving as an early marker of neurodegenerative diseases. Reliable confirmation of such events requires both polysomnographic (PSG) signals and video observation, but synchronizing these modalities outside laboratory settings remains a challenge. This work presents a MATLAB application that integrates European Data Format (EDF) signals with MP4 recordings through an intuitive graphical interface. The system enables simultaneous navigation of electrophysiological data and video, supported by signal preprocessing, artifact reduction, and timeline synchronization. Researchers can use the tool to align multimodal recordings and collaboratively review events with clinicians, ensuring more consistent interpretation. By bridging technical and clinical perspectives, the application reduces manual workload, supports longitudinal studies, and promotes reproducibility in multimodal sleep research. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.