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
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
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.

Abstract
Due to the susceptibility of Artificial Intelligence (AI) to data perturbations and adversarial examples, it is crucial to perform a thorough robustness evaluation before any Machine Learning (ML) model is deployed. However, examining a model's decision boundaries and identifying potential vulnerabilities typically requires access to the training and testing datasets, which may pose risks to data privacy and confidentiality. To improve transparency in organizations that handle confidential data or manage critical infrastructure, it is essential to allow external verification and validation of Al without the disclosure of private datasets. This paper presents Systematic Pattern Analysis (SPATA), a deterministic method that converts any tabular dataset to a domain-independent representation of its statistical patterns, to provide more detailed and transparent data cards. SPATA computes the projection of each data instance into a discrete space where they can be analyzed and compared, without risking data leakage. These projected datasets can be reliably used for the evaluation of how different features affect ML model robustness and for the generation of interpretable explanations of their behavior, contributing to more trustworthy AI.

Abstract
The increasing energy demand in seaport operations, driven by electrification and decarbonisation targets, requires enhanced tools for operational planning and flexibility management. This paper proposes a novel centralised Energy Management System designed for seaports, which, unlike previous approaches that mainly focused on cost minimisation jointly optimises Battery Energy Storage System scheduling, energy and reserve market participation, and carbon-intensity reduction. A key contribution of this work is the integration of CO\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$_2$$\end{document} emission forecasts and day-ahead market data into a multi-objective formulation, allowing the Energy Management System not only to minimise operational costs but also to reduce indirect emissions. Additionally, a Traffic Light system is proposed to support operators' decision-making by providing actionable flexibility guidelines. A case study based on real-world data from the Port of Sines shows that this method achieves at least an 17% reduction on an annual basis compared to baseline operations, while ensuring cost efficiency. Results highlight the Energy Management System's potential as a decision-support tool for port authorities seeking to align operational efficiency with sustainability goals.