Siste publikasjoner
Oversikt over siste publikasjoner fra Avdeling LOG, registrert i Cristin/NVA.
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Barros, Sonia; Carvalho, Ana; Macedo, Guilherme; Ferreira, Joao C. & Mascarenhas, Miguel
(2026).
Artificial Intelligence and AI-Adjacent Digital Technologies in Nursing Practice Within Gastroenterology: A Scoping Review of Current Applications, Challenges, and Future Directions.
Nursing Reports.
ISSN 2039-439X.
16(10).
doi:
10.3390/nursrep16100357.
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Background/Objectives: Artificial intelligence (AI) is increasingly integrated into clinical gastroenterology, with emerging applications in nursing contexts. However, the extent and nature of these applications in gastroenterology nursing remain under-explored. This scoping review aimed to map the current landscape of AI and closely related AI-adjacent digital technologies in gastroenterology nursing. Methods: Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guideline, PubMed, Medline, Web of Science, and Scopus were searched for peer-reviewed, English-language studies published since 2000, and the findings were synthesized narratively. Results: Of 184 records identified, 11 studies were included, highlighting the potential of these technologies in clinical decision support, workflow optimization, patient education, and endoscopy assistance. Reported benefits include gains in workflow efficiency, technical accuracy, and reduced administrative workload; direct evidence of improved nursing-led patient outcomes was reported in only a minority of studies. Challenges encompass limited nurse-led research, insufficient external validation, implementation gaps, and ethical concerns. Conclusions: Greater nurse involvement in AI development is essential to drive paradigm shifts in clinical practice. This review synthesizes evidence to inform future applications, research, and policy in AI-enhanced gastroenterology nursing.
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Korani, Zahra Mardani; Smet, Thimoty; Challenger, Moharram; Moin, Armin; Ferreira, Joao C. & Silva, Alberto Rodrigues da
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(2026).
ML2++ in Practice: A Use-Case-Aware Study of Modeling Effort and Usability for Forecasting-Enabled IoT/CPS.
I Abrahão, Silvia; Cheng, Betty H.C.; Ciccozzi, Federico; Reinhartz-Berger, Iris; Bork, Dominik; Chakraborty, Shalini; Arcega, Lorena; Rutle, Adrian; Ruscio, Davide Di; Pérez, Francisca; Clarisó, Robert; Garcia, Joan; Michael, Judith; Alfonso, Iván & Vega, Alfonso de la (Red.),
MODELS Companion '26: Proceedings of the ACM/IEEE 29th International Conference on Model Driven Engineering Languages and Systems.
Association for Computing Machinery (ACM).
ISSN 9798400729034.
s. 1145–1154.
doi:
10.1145/3837062.3839559.
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ML2++ generates executable forecasting pipelines for IoT/CPS applications from textual or graphical models. This paper evaluates ML2++ from three complementary perspectives: an artifact-based technical evaluation across river-flow, smart-home energy, and solar-power use cases; a three-stage user study (n = 8) comparing manual Python development plus IoT-template integration with guided adaptation of a prepared textual ML2++ model; and a separate graphical-editor study (n = 8, disjoint participants). Among the five participants who completed Stage 3, four reported less time than for the combined manual baseline, although fixed study order, self-reported timing, extreme observations, and partial completions make this result exploratory rather than causal. Comparison with template reuse alone did not show a general Stage 3 reduction. The stronger finding concerns residual difficulty: across the studies, effort concentrates in model-instance authoring, behavior and configuration modeling, and compiler feedback once coding and integration are automated. The graphical workflow was associated with fewer hand-written artifacts and fewer reported errors, while behavior modeling and diagnostics remained challenging. Overall, the evidence suggests that ML2++ shifts engineering effort from implementation and integration toward explicit, inspectable model authoring, highlighting concrete targets for future tooling improvements.
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Marcucci, Edoardo & Gatta, Valerio
(2026).
Weaving stories into policies: integrating storytelling in urban freight participatory planning.
Transport Policy.
ISSN 0967-070X.
doi:
10.1016/j.tranpol.2026.104455.
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Urban freight transport remains only partially integrated into Sustainable Urban Mobility Plans, despite its economic importance and growing environmental and social implications. Existing research points to methodological weaknesses, fragmented governance, and limited stakeholder engagement, but these problems have rarely been examined jointly through collective-action and narrative perspectives. This paper conceptualises urban freight as a collective-action problem shaped by coordination failures, conflicting incentives, and weak mutual assurance. Using a research-question-driven integrative conceptual methodology combining mobility planning, collective-action theory, and narrative studies, the paper develops a six-step narrative-based participatory planning framework. Storytelling is treated not as a stand-alone communication tool, but as a mechanism embedded in participatory governance and linked to diagnosis, deliberation, policy translation, implementation, and learning. The framework offers planners and policymakers a structured way to connect technical evidence with stakeholder cooperation while providing a basis for future empirical testing.
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Ferreira, Joao C. & Elvas, Luìs B.
(2026).
Time-Aware Machine Learning for Prospective Tachycardia Prediction in Cardiovascular Disease.
Intelligence-Based Medicine.
doi:
10.1016/j.ibmed.2026.100482.
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Tachycardia can accompany acute deterioration in hospitalized patients with cardiovascular disease, yet most machine-learning studies emphasize detection rather than prospective prediction. Using routinely collected physiological monitoring data from a hospital-wide database of 512,764 patients, we identified 968 patients with myocardial infarction (MI; n = 260), pulmonary thromboembolism (PTE; n = 48), or aortic stenosis (AS; n = 660) and developed models for prospective tachycardia prediction. Tachycardia was defined as heart rate >100 bpm. Historical physiological features were separated temporally from the future target, population-level autocorrelation analysis informed lag selection, and Random Forest (RF), Naïve Bayes (NB), and a feedforward neural network (NN) were evaluated using patient-blocked, time-aware validation. At the selected 90-step lag configuration, RF showed the strongest performance, including an F1-score of 0.89 ± 0.02, PR-AUC of 0.72 ± 0.03, and AUROC of 0.93 ± 0.02 for PTE. These findings provide internal feasibility evidence for time-aware tachycardia risk prediction from routine hospital monitoring data; external and prospective validation is required before clinical deployment.
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Ferreira, Joao C. & Rosa, Isabel
(2026).
Large language models in healthcare: applications, evaluation frameworks, and governance pathways — a scoping review and multidimensional framework.
Frontiers in Digital Health.
8.
doi:
10.3389/fdgth.2026.1865568.
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Background Large language models (LLMs) are increasingly evaluated for healthcare applications spanning clinical documentation, decision support, patient communication, research assistance, and operational workflows. Despite rapid adoption interest, evidence remains heterogeneous and standardised approaches for evaluation and governance are not yet consistently applied. Objective To map healthcare applications of LLMs, synthesise reported outcomes and risks, and propose a multidimensional evaluation and governance framework oriented toward digital-health implementation. Methods A scoping review was conducted following the methodological guidance of Arksey and O'Malley, Levac et al., and the Joanna Briggs Institute, with reporting aligned to the PRISMA-ScR checklist. The protocol was prospectively registered on the Open Science Framework https://doi.org/10.17605/OSF.IO/SWP78 ). Searches were performed in PubMed/MEDLINE, Embase, Scopus, Web of Science, IEEE Xplore, ACM Digital Library, and the ACL Anthology, supplemented by medRxiv/bioRxiv preprint searches and grey-literature scanning of WHO, FDA, and EU AI-Office guidance, covering 1 January 2019–30 April 2026. Title/abstract and full-text screening were carried out in duplicate; inter-rater agreement was Cohen's κ = 0.78. Data were extracted in duplicate using a piloted form. Synthesis followed Braun and Clarke's six-phase reflexive thematic analysis. The complete list of 78 included studies is provided in Supplementary File S4. Results Seventy-eight studies met inclusion criteria; 68% were published between 2023 and 2025. Five application domains were identified: (1) clinical documentation and summarisation; (2) clinical decision support and reasoning assistance; (3) patient communication and health-literacy support; (4) biomedical research and knowledge synthesis; and (5) administrative and operational use cases. The evidence base was dominated by benchmark and simulated-workflow studies (89%), with limited prospective workflow-embedded evaluations (11%). Reported benefits concentrated on documentation efficiency, text quality, and knowledge synthesis; safety-relevant risks included hallucinated content, omission of clinically critical information, demographic bias, privacy vulnerabilities, limited explainability, and automation bias. Studies were geographically concentrated in North America and East Asia, with limited representation from Sub-Saharan Africa, South Asia, and Latin America. Conclusions Current evidence supports cautious deployment of LLMs in selected healthcare tasks under structured oversight. Translational progress depends on prospective evaluation, standardised reporting, equity-focused audits, and lifecycle governance with continuous monitoring. The proposed five-dimensional framework (technical performance, clinical validity, equity, workflow integration, governance) coupled with a three-tier risk model is intended to support researchers and healthcare organisations in assessing readiness and implementing LLM-enabled tools responsibly.
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Kubaščík, Michal; Aggarwal, Swati; Karpiš, Ondrej; Tupý, Andrej; Šarafín, Peter & Chochul, Miroslav
(2026).
EEG-based hypoglycemia detection in Type 1 Diabetes: Proof-of-concept study.
Frontiers in Human Neuroscience.
20.
doi:
10.3389/fnhum.2026.1852403.
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Assunção, Lucas; Urrutia, Sebastián & Santos, Andréa Cynthia
(2026).
Kernel pump : boosting the convergence of the feasibility pump through a kernel search-based decomposition.
Mathematical Programming Computation.
ISSN 1867-2949.
doi:
10.1007/s12532-026-00333-2.
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Abstract Feasibility Pump (FP) is a well-studied mixed integer linear programming primal heuristic that sequentially guides fractional solutions into integer feasibility by minimizing their distance to given target integer points, while preserving the linear constraints of the original problem. Due to its effectiveness and simplicity, FP has been embedded in most commercial solvers, and several variants of the basic method have been proposed, aiming at improving its convergence and the quality of the solutions obtained. This work introduces Kernel Pump (KP), a novel FP speed-up technique that relies on the intuition that not all binary variables are active (i.e., set to one) in a feasible solution, and can, thus, be sub-divided in a kernel search fashion. Precisely, KP identifies a subset of promising binary variables (the kernel) and distributes the rest into buckets ranked by their likelihood of composing a feasible/optimal solution. FP sub-problems are then solved iteratively, focusing on the current kernel and progressively adding variables from the buckets whenever necessary. Extensive computational experiments on benchmark instances from MIPLIB 2017, as well as two additional problems from the literature of vehicle routing and scheduling, reveal a considerable boost in success rate, also decreasing running times for the majority of instances tested. Notably, aside from outperforming FP, the new method was able to find feasible solutions in cases where CPLEX failed within an hour of search. To our knowledge, this is also the first study to introduce a decomposition approach within the FP framework.
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Ferreira, Joao C. & Elvas, Luìs B.
(2026).
Machine learning-based early detection of abnormal heart rate in critically ill patients: a real-world clinical dataset study with automated alert system integration.
Frontiers in Artificial Intelligence.
9.
doi:
10.3389/frai.2026.1879609.
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Introduction: Cardiovascular diseases (CVD) are a leading global health challenge, particularly in resource-constrained settings where delayed diagnosis worsens outcomes. Abnormal heart rate (tachycardia/bradycardia) is a modifiable, independent risk factor that benefits from early, automated detection.Methods: We used a large real-world dataset from Hospital Santa Maria, Lisbon (23,122 patients, January 2019–November 2021, spanning the COVID-19 period). Two operational outcome definitions were evaluated: (i) an interquartile-range (IQR)-based, patient-specific definition, used to compare four classifiers (Logistic Regression, K-Nearest Neighbors, Random Forest, Naive Bayes), and (ii) standard clinical thresholds for tachycardia/bradycardia, applied to the best-performing model. Following CRISP-DM, models were trained and evaluated with patient-grouped, walk-forward time-series cross-validation to prevent patient-level data leakage.Results: Logistic Regression was the best-performing model. Under the IQR-based definition: AUROC 0.78, recall 85.4%, precision 66.1%, F1-score 74.5%. Under the clinically standard threshold-based definition: AUROC 0.81, recall 91.7%, precision 68.8%, F1-score 78.6%. Expanded metrics (event prevalence, false-positive rate, alert burden, calibration, confidence intervals) are reported. A prototype real-time e-mail alert system was demonstrated as a feasibility proof-of-concept and reviewed informally by two cardiovascular specialists.Discussion: Recall and AUROC alone do not establish clinical utility. Key limitations include the single-centre design, substantial missingness in ethnicity/diagnosis variables, the small clinician-evaluation sample (n = 2), and pandemic-period data collection. This work is presented as a methodologically strengthened proof-of-concept, with prospective, multi-site, multi-clinician external validation identified as the necessary next step.
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Marcucci, Edoardo; Gatta, Valerio; Maltese, Ila; Pasquarelli, Giovanni & Balla, Bhavani Shankar
(2026).
The Invisible Riders: Platform Work, Labour Conditions and Sustainable Last-Mile Delivery in Rome.
Transport Policy.
ISSN 0967-070X.
189(December).
doi:
10.1016/j.tranpol.2026.104420.
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The rapid growth of platform-based food delivery has reconfigured urban freight transport by reshaping last-mile operations, labour conditions and sustainability challenges in cities. Research has only partially examined how social, environmental and economic outcomes interact within these platform-mediated delivery systems. This paper addresses this gap by analysing food-delivery riders through a Triple Bottom Line framework and drawing implications for sustainable urban freight policy. Using 402 rider interviews conducted in Rome, the study combines observable-group profiling and Latent Class Cluster Analysis to compare job satisfaction, perceived accident risk, fatigue, operational carbon dioxide emissions, earnings and tipping outcomes. Three motivational profiles emerge: Flexibility-First Freelancers, Necessity-Driven Breadwinners and Convenience-Adaptive Seekers. Results show that rider citizenship status, platform use and work motivation are associated with different sustainability outcomes. Riders with Italian citizenship report higher perceived accident risk and higher estimated operational carbon dioxide emissions. Multiple-platform riders report higher earnings and higher estimated operational emissions. Necessity-driven riders combine weaker welfare conditions with no statistically distinct earnings advantage. Convenience-Adaptive Seekers show lower estimated operational emissions and higher tip frequency. These findings suggest that sustainable urban freight policy should not focus only on operational efficiency and emissions reduction. It should also address labour conditions, safety and distributional inequalities in platform-based delivery markets. The paper contributes a rider-centred perspective and argues that sustainable last-mile policy begins by making the workers visible on whom the system depends.
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Alzoubi, Yehia & Mishra, Alok
(2026).
Assessing Privacy Risks of Proprietary and Open-Weight Large Language Models: A GDPR-Oriented Comparative Analysis.
Journal of Responsible Technology.
27(September).
doi:
10.1016/j.jrt.2026.100187.
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This study examines the growing privacy risks posed by Large Language Models (LLMs) through a structured, General Data Protection Regulation (GDPR)-oriented comparative analysis. As LLMs increasingly process personal data across sectors, understanding their alignment with the proposed GDPR-based evaluation dimensions is essential for responsible adoption and risk mitigation. Using a multi-criteria evaluation framework informed by the literature, six leading LLMs (GPT-4/5, Claude 3, Gemini, Llama 3, Mistral, and Falcon) were assessed across six GDPR-based evaluation dimensions. Weighted scoring enabled a transparent comparison of privacy governance maturity across the evaluated models. Findings indicate strong performance in security and risk-based controls, particularly among proprietary models, while notable gaps remain in Purpose Limitation, Data Minimization, and documentation of lawful bases. Proprietary models generally demonstrated higher privacy governance maturity, whereas open-weight models benefited from community scrutiny but lacked structured documentation and enterprise accountability. Data Subject Rights exhibited mixed support across all evaluated models, reflecting technical challenges in implementing access, rectification, and erasure within machine learning contexts. This study contributes an actionable framework for organizations, policymakers, and researchers seeking to align Artificial Intelligence (AI) deployment with GDPR requirements. Future research should focus on longitudinal assessments of evolving LLM policies, the development of AI-specific regulatory guidance to address ambiguities in the GDPR, and the creation of standardized benchmarks for evaluating privacy governance in foundation models.
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Skagerstam, Bo-Sture & Rekdal, Per Kristian
(2026).
Fractional angular momentum and quasi-probability densities for angular degrees of freedom.
APL Quantum.
3(3).
doi:
10.1063/5.0338403.
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In the present study, we consider quasi-probability densities W[θ, p] and W1/2[θ, p] for pure states depending on two classical parameters θ and p, where −π ≤ θ ≤ π and p can take any real number. For integer values of p, the corresponding marginal distributions W[p] and W1/2[p] are positive and in accordance with Born’s rule in quantum mechanics for an angular momentum observable L. For the state ψ(θ) in Skagerstam and Rekdal [J. Opt. 26, 095201 (2024); arXiv:2312.16535v1 [quant-ph]], which can have half-integer angular momentum expectation values, negative values of W[θ, p] and W1/2[θ, p] can reveal non-classical features of ψ(θ) but in an ambiguous manner. In line with Franke-Arnold et al. [New J. Phys. 6, 103 (2004)], it is shown that experimental data of the uncertainties Δθ and ΔL can be sufficient to reveal quantum-mechanical features of such states ψ(θ) without necessarily making use of quasi-probability densities.
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Ervik, Joanna Bardelosa; Mwesiumo, Deodat Edward; Kvadsheim, Nina Pereira & Andersen, Terje Lauvaas
(2026).
Public procurement of low-carbon transport: A systematic review and conceptual framework of enablers, constraints, and intervention pathways.
Research in Transportation Economics.
ISSN 0739-8859.
119.
doi:
10.1016/j.retrec.2026.101832.
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Public procurement is a key lever for advancing low-carbon transport; however, evidence on its enablers and constraints remains fragmented. This systematic literature review of 35 empirical studies applies thematic analysis grounded in transaction cost economics and institutional theory to address that gap. The findings show that procurement decisions are shaped by the interaction between economic and institutional factors. Policy and regulatory pressures and environmental benefits are the most widely cited enablers, while high upfront costs, infrastructure limitations, and information gaps are the dominant constraints. These factors do not operate independently; institutional pressures shape how risks are perceived, while transaction costs determine whether policy signals translate into adoption. Based on these findings, this study proposes a conceptual framework explaining procurement through three pathways: financial, infrastructure, and information. The relative importance of coercive, normative, and mimetic pressures shifts as procurement experience accumulates. Notably, the information pathway becomes self-reinforcing, as each successful procurement reduces uncertainty and transaction costs for subsequent adopters through knowledge dissemination. The findings suggest that policy instruments should be sequenced according to the stage of adoption. Coercive mandates and financial support are most effective during early adoption, while information infrastructure and normative mechanisms become increasingly important as operational evidence accumulates.
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Pira, Saeid & Hansson, Lisa
(2026).
Revisiting the 15-min city through accessibility assessment: A critical systematic review of FMC-based studies.
Cities: The International Journal of Urban Policy and Planning.
ISSN 0264-2751.
179.
doi:
10.1016/j.cities.2026.107516.
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Proximity-centered planning has recently attracted increasing attention. The 15-min city (FMC) and related X-minute frameworks are promoted as strategies for advancing sustainable and equitable urban development. However, limited attention has been paid to how major critiques of the FMC are reflected in empirical studies that apply the framework to accessibility assessment. Rather than evaluating accessibility outcomes, this review examines how accessibility is operationalized and assessed in FMC-based research. To address this gap, this study conducts a systematic and critical review of 149 publications and develops a structured analytical framework across five domains: operationalization and methodological design, social equity and inclusion, implementation and feasibility, governance and participation, and service quality and scale. By organizing the literature around key challenges, the review identifies persistent blind spots in FMC-based accessibility research. The findings indicate that most studies rely on a 15-min threshold, employ quantitative methods, and prioritize residential and leisure amenities, while employment and education remain underexamined. Accessibility is predominantly measured through potential and objective approaches, with limited attention to realized and subjective dimensions. Socio-demographic diversity, multimodal transport integration, stakeholder participation, and service quality are also insufficiently addressed. These findings demonstrate that current applications capture only part of the multidimensional nature of accessibility embedded in the FMC framework. By assessing how recurring critiques are reflected in accessibility assessment practices, this review provides insights for developing more inclusive and context-sensitive accessibility frameworks. Future research should prioritize context-sensitive thresholds, adaptable service classifications, multimodal integration, and participatory approaches to strengthen FMC-based accessibility assessments across diverse contexts.
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Polami, Komeyl Baghizadeh; Pahl, Julia & Ribas, Paulo Cesar
(2026).
Data-Driven Fuel Consumption Analysis for Maintenance Operations in Offshore Wind Farms.
I Armas, Jesica de & Ruiz, Eduardo Lalla (Red.),
Logistics and Maritime Systems: 14th International Conference, LOGMS 2026, held in conjunction with EUROMar 2026, Barcelona, Spain, July 7–10, 2026, Proceedings.
Springer.
ISSN 9783032307958.
s. 143–155.
doi:
10.1007/978-3-032-30795-8_13.
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This study presents a data-driven analysis of fuel consumption in offshore wind farm (OWF) maintenance operations using high-frequency engine logs, navigation records, met-ocean data, and operator-reported activity logs from a service offshore vessel (SOV). The results show that transit-related activities account for nearly three-quarters of total mission fuel consumption. Based on this finding, predictive modeling is focused on transit operations, where the greatest potential for fuel and emission reduction exists. Among the evaluated models, Random Forest (RF) achieved the best performance, with an R2 of 0.857, MAE of 3.412 L/h, and RMSE of 8.917 L/h on unseen test data. Feature importance analysis shows that vessel speed is the dominant predictor, followed by wave-related variables. These findings indicate that weather-aware routing and speed selection can support lower fuel use, CO2 emissions, and maintenance costs in OWF logistics.
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Rubio-Alcala, Fernando D.; Molka-Danielsen, Judith Ann & Delgado-Algarra, Emilio J.
(2026).
Fostering multilingualism and intercultural engagement in higher education.
Journal of International Students.
ISSN 2162-3104.
16(17),
s. 25–49.
doi:
10.32674/88y21426.
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Over its 40 years, the Erasmus program has significantly shaped the European Higher Education Area. However, many students show limited integration into host languages and cultures, often socializing mainly with other exchange students and relying on English. As international relations offices play a key role in fostering integration, this study combines a systematic literature review with an analysis of university initiatives to identify effective institutional practices. Following PRISMA guidelines and the PICO framework, it examines peer-reviewed studies and institutional actions supporting multilingualism and intercultural engagement. Findings highlight ten successful initiatives, including Erasmus Student Networks, Buddy Programs, Tandem exchanges, and Language cafés. Results also reveal significant variability across institutions in organizational structures and access to resources. Overall, the study identifies practical actions that enhance social integration, cultural participation, and multilingual development, offering guidance for improving international student experiences in higher education.
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Mannari, Chiara; Turchi, Tommaso; Versino, Serena; Bacco, Manlio; Ferrari, Alessio & Conati, Cristina
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(2026).
Assessing computational thinking through integrated approaches: the case of ModeLLer.
Behaviour and Information Technology.
ISSN 0144-929X.
doi:
10.1080/0144929X.2026.2711028.
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Digital technologies and Artificial Intelligence increasingly shape workplace environments, making Computational Thinking (CT) relevant beyond traditional educational settings. This paper addresses the challenge of CT assessment through the evaluation of ModeLLer, a web-based modelling tool designed to support end users, i.e. users with no prior knowledge of semi-formal modelling languages, to formalise domain knowledge. Grounded in End-User Development (EUD) principles, ModeLLer features a block-based editor to represent domain entities and relationships and automatically translates user-generated blocks into standard diagrams in UML, BPMN, and iStar. To evaluate the tool, we conducted a user study involving 30 participants, based on a scenario of agricultural digitalisation. The outcomes were evaluated using a multi-approach strategy combining expert inspection of the tool, artefact-based analysis of user-generated models, CT profiles, workload measures, and log-based analysis of user interactions. An integrated analysis highlights the mutual relationship between tool capabilities, user skills, and artefacts, as well as informing the design of adaptive features. Overall, the study offers empirical insights into CT assessment within EUD, contributing to evaluation practices and the design of AI-powered EUD tools.
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Ferreira, Joao C.
(2026).
Blockchain for traceability in political lobbying: empirical insights from stakeholder surveys on transparency problems and solutions.
Frontiers in Blockchain.
9.
doi:
10.3389/fbloc.2026.1870518.
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Transparency in public affairs interactions between companies and governments is critical to democratic legitimacy, yet existing lobby registers suffer from fragmented reporting, weak record integrity, limited traceability, and compliance gaps. This paper reports a Design Science Research (DSR) study that develops and evaluates a permissioned blockchain architecture for mandatory Public Affairs transparency. Two stakeholder surveys provided empirical grounding: Survey 1 (N = 61 domain professionals) elicited functional, non-functional, and GDPR compliance requirements, while Survey 2 (N = 14 practitioner evaluators) assessed a proof-of-concept implementation on Hyperledger Fabric following a live demonstration. Findings reveal widespread concerns over non-repudiation and auditability in current systems — 87% rated existing record integrity as weak—alongside strong endorsement for blockchain’s immutability, versioned audit trails, and hybrid on-/off-chain design to ensure GDPR-aligned traceability. Post-demonstration evaluation achieved a mean score of 4.6/5 for traceability and integrity, and 86% of evaluators recommended real-world piloting. The study makes three contributions: (i) an empirically derived requirements model and information-lifecycle framework; (ii) a hybrid permissioned-blockchain blueprint implemented on Hyperledger Fabric; and (iii) a replicable stakeholder-centric DSR methodology for sociotechnical artefact design in regulated governance contexts.
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Ferreira, Joao C. & Gonçalves, Frederica
(2026).
Blockchain-enabled Model Context Protocol (MCP) for trust and consent in LLM-orchestrated sports analytics.
Journal of Sports Analytics.
ISSN 2215-020X.
12,
s. 1–26.
doi:
10.1177/22150218261448184.
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The Model Context Protocol (MCP) enables large language models to query and synthesize sports data across distributed sources, yet it lacks built-in mechanisms for provenance, integrity, and athlete-controlled access. This study proposes a hybrid MCP–blockchain architecture for LLM4Sports , a domain-specific model fine-tuned on localized sports datasets. In this design, MCP orchestrates multi-database retrieval, while a blockchain layer immutably anchors query and data-bundle hashes. Smart contracts manage time-bound and granular consent through decentralized identifiers and verifiable credentials, and auditable logs ensure end-to-end traceability. We contribute: (i) a trust-enhanced reference architecture combining MCP and blockchain; (ii) reusable prompt and workflow templates that embed consent validation within natural-language tasks (e.g., “Summarize athlete X's performance with verified consent”); and (iii) prototype implementations for both team analytics and personalized coaching. Evaluations on synthetic but realistic workloads show high verification accuracy and minimal orchestration overhead, demonstrating the feasibility of real-time, consent-aware analytics. The proposed framework enhances interoperability, regulatory compliance (e.g., GDPR), and athlete autonomy, while bolstering practitioner trust. We conclude by discussing scalability, legacy integration, and privacy trade-offs, and by outlining next steps toward field pilots and multimodal extensions (e.g., video provenance) across professional and amateur sports ecosystems.
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Ferreira, Joao C.; Rosa, Isabel & Correia, Ricardo
(2026).
Text2FHIRwallet: Automated Generation of FHIR Patient Summaries from Unstructured Cardiology Reports Using Fine-Tuned Portuguese Language Models—Development and Evaluation of a Health Professional Wallet.
Applied Sciences.
16(15).
doi:
10.3390/app16157795.
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Background and Objectives: Cardiology departments generate large volumes of unstructured free-text reports that impose substantial manual review burdens on clinicians; at Hospital de Santa Maria—Portugal’s largest public hospital—manual review of 12,651 reports took approximately seven minutes per report, representing over 1475 h of avoidable administrative work. This study presents Text2FHIRwallet, a health professional digital wallet that automates extraction and structuring of clinical entities from unstructured Portuguese cardiology reports using fine-tuned Named Entity Recognition (NER) models and maps the results to Fast Healthcare Interoperability Resources (FHIR) R4 patient summaries. Materials and Methods: Following the Design Science Research Methodology (DSRM) and CRISP-DM, we fine-tuned four transformer-based models—BERTimbau Base, BERTimbau Large, Albertina PT-PT, and MediAlbertina—on 305 manually annotated cardiology reports (77,309 tokens; κ = 0.85 inter-annotator agreement) covering eight clinical entity types, drawn from a corpus of 12,651 anonymised documents. Entities were mapped to FHIR R4 resources and delivered through a secure, role-based mobile wallet (React Native). Evaluation comprised token-level NER benchmarking with bootstrapped confidence intervals and McNemar’s testing, FHIR mapping accuracy assessment on 100 manually reviewed reports, processing-efficiency measurement, and a usability pilot with 10 cardiologists (SUS, NPS). Results: MediAlbertina achieved the highest NER performance (macro F1 = 0.985, 95% CI: 0.979–0.990), significantly outperforming all baseline models (p < 0.01, McNemar’s test) and comparing favourably with—though not directly comparable to, given differing languages and datasets—published benchmarks such as GPT-4 (F1 = 0.962 in ophthalmology NER) and fine-tuned BERT models for lung cancer NER (F1 ≈ 0.85–0.90). FHIR mapping accuracy was 98% on 100 independently reviewed reports. Report processing time was reduced from approximately seven minutes to 15–30 s (93–96% reduction), with peak batch-inference throughput of up to 1000 reports/h under parallelised GPU load (observed end-to-end throughput in pilot deployment was approximately 250 reports/h). The pilot usability evaluation yielded a SUS score of 87 (excellent) and an NPS of 80. Conclusions: Text2FHIRwallet demonstrates that domain-specific fine-tuning of a Portuguese-language pretrained language model achieves near-ceiling clinical NER accuracy, enabling scalable, interoperable, and privacy-compliant patient summary generation from unstructured cardiology text, offering an end-to-end pathway for integrating AI-driven NLP into clinical workflows and FHIR-based health information ecosystems, with implications for administrative efficiency, care coordination, and clinical research in non-English-language settings.
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Gatta, Valerio; Iannaccone, Gabriele & Marcucci, Edoardo
(2026).
Combining quantitative and qualitative approaches in urban freight participatory planning.
Transportation Research Procedia.
98,
s. 108–114.
doi:
10.1016/j.trpro.2026.04.111.
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Urban freight transport is essential for ensuring the timely delivery of goods within cities. However, its operations generate
significant environmental and societal costs due to private sector dominance, fragmented stakeholder groups, and conflicting
objectives. Addressing these issues requires implementing long-term participatory planning frameworks capable of prioritizing
effective and acceptable policies while balancing public and private interests. Stakeholder participation plays a pivotal role in
overcoming these challenges by fostering collaboration, building consensus, and enhancing the effectiveness of planning processes.
This paper systematically classifies and evaluates methods for policy prioritization. A comprehensive review highlights the
strengths and limitations of both quantitative and qualitative approaches. The paper also discusses the potential of combining these
approaches, providing actionable insights for policymakers and practitioners to develop robust urban freight participatory planning
processes.
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Ferreira, Joao C.
(2026).
Edge-Assisted Federated Learning with Verifiable Data Provenance for Remote Patient Monitoring over Constrained Networks.
I Ukjent (Red.),
2026 IEEE 18th International Conference on Computational Intelligence and Communication Networks (CICN).
IEEE (Institute of Electrical and Electronics Engineers).
ISSN 9798331546519.
s. 1304–1309.
doi:
10.1109/cicn70047.2026.11594160.
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Remote patient monitoring (RPM) increasingly relies on edge devices and intermittent wireless links, creating tension between model quality, privacy, and communication cost. We propose an edge-assisted federated learning (FL) framework that (i) reduces uplink bandwidth through update compression and link-quality-aware client selection, and (ii) adds verifiable provenance of training contributions through lightweight, tamper-evident logging suitable for multistakeholder health ecosystems. A prototype evaluated under emulated constrained links and non-IID physiological timeseries data shows that network-aware orchestration reduces communication volume by ∼46% and reaches the target accuracy in fewer rounds while preserving predictive performance. Provenance logging adds <2% communication overhead and negligible compute cost, enabling auditability of client contributions in bandwidth-constrained or operationally fragmented RPM deployments.
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Marazi, Naveed Farooz; Majumdar, Bandhan Bandhu; Sahu, Prasanta K.; Marcucci, Edoardo & Gatta, Valerio
(2026).
For whom the road tolls: perceived benefits, commuter segmentation, and congestion pricing acceptability in Hyderabad.
Case Studies on Transport Policy.
ISSN 2213-624X.
25.
doi:
10.1016/j.cstp.2026.101904.
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This paper investigates commuter perceptions of congestion pricing (CP) benefits in Hyderabad, India. Previous experience suggests that limited knowledge of how users perceive CP before implementation may undermine policy adoption. Identifying and examining car users’ perceived CP-related benefits constitutes a key research question. Based on a face-to-face survey yielding 435 complete responses, the study employs benefit-based commuter segmentation and Meta Ranking to identify and prioritize perceived advantages across heterogeneous user groups. Results show that, although travel-time minimization emerges as the most important perceived benefit overall, respondents are far from homogeneous, with four identified commuter segments prioritizing different benefit bundles. These findings underscore the need for a differentiated approach to policy design and communication, while also highlighting the role of stakeholder engagement and transparent revenue use to promote CP acceptability. The proposed methodology offers a scalable tool for context-specific policy analysis aimed at maximizing the ex-ante probability of acceptance.
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Marcucci, Edoardo; Lozzi, Riccardo & Gatta, Valerio
(2026).
Crafting Consensus in City Logistics: The Power of Storytelling in Stakeholder-Driven Freight Policy Design.
Transportation Research Procedia.
98,
s. 52–59.
doi:
10.1016/j.trpro.2026.04.104.
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This paper examines the role of storytelling in participatory planning for urban freight transport. Stakeholder engagement is universally recognized as fundamental to urban logistics governance, yet its practical implementation remains uncertain. The research explores how digital storytelling, integrated within the Living Lab environment through a purpose-built software, enhances dialogue, consensus-building, and policy acceptance. In particular, the study analyzes its application within the Sustainable Urban Logistics Plan of the Metropolitan City of Rome, using it as a case study to clarify the effectiveness of this tool. The results demonstrate that storytelling increases stakeholder satisfaction and engagement, supporting a more transparent and inclusive participatory planning process. Participants in the experiment were real urban freight stakeholders, such as shippers, receivers, and transport providers, engaged in the co-creation of actual metropolitan logistics policies. These findings advocate for broader adoption of storytelling in urban freight transport governance to improve stakeholder sense making and policy effectiveness.
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Fazendas, Paula; Bairros, Rita; Elvas, Luìs B.; Brochado, Liliana; Ferreira, Joao C. & Gomes, Rita
[Vis alle 13 forfattere av denne artikkelen]
(2026).
Artificial intelligence–based calcium scoring using 3D transesophageal echocardiography in aortic stenosis: a pilot study.
Journal of Cardiovascular Imaging.
ISSN 2586-7210.
34(1).
doi:
10.1186/s44348-026-00080-x.
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Abstract Background Aortic valve calcium scoring by computed tomography (CT) is an established method for assessing aortic stenosis severity but is limited by radiation exposure and availability. Artificial intelligence (AI)-based calcium detection using transthoracic echocardiography has shown promise but depends on acoustic window quality. Transesophageal echocardiography (TEE), particularly 3D TEE, may overcome these limitations by providing improved visualization without radiation. The objective of this study is to evaluate the feasibility of AI-based quantification of aortic valve calcium using 3D TEE. Methods In this prospective pilot study, 23 patients (median age, 76 years; 56.5% male) with moderate or severe aortic stenosis underwent 3D TEE and CT. Multiplanar reconstruction generated 1.5-mm diastolic short-axis slices. A computer vision–based model identified calcium-related speckles. An automated TEE calcium score was derived from the sum of calcium pixels across 11 frames per patient, which was compared with the CT Agatston score. Results The TEE calcium score showed a significant positive correlation with CT Agatston scores (r = 0.65, P < 0.001). Receiver operating characteristic analysis yielded an area under the curve of 0.87 (95% confidence interval, 0.69–1.00) for identifying severe calcification (cutoff, 68,813 pixels; sensitivity, 89.5%; specificity, 75.0%). Conclusions AI-based calcium quantification using 3D TEE is feasible and correlates with CT-derived scores. This radiation-free approach may provide a promising alternative for assessing aortic valve calcification.
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Ferreira, Joao C. & Faria, Pedro
(2026).
Automated video-based AVPU assessment within a FHIR-enabled clinical decision support framework.
Scientific Reports.
doi:
10.1038/s41598-026-57122-z.
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Abstract Accurate and timely assessment of consciousness is critical for triage, escalation of care, and patient safety in emergency and hospital settings. However, documentation using the AVPU scale (Alert, Verbal, Pain, Unresponsive) remains inconsistent owing to high workload, subjectivity, and fragmented workflows. This study developed and evaluated Consc.ia, a video-based clinical decision-support platform that automates AVPU inference while preserving clinician oversight and enabling seamless, interoperable documentation through HL7 FHIR. A simulated AVPU dataset comprising 136 videos from 58 healthcare professionals (physicians, nurses, paramedics, and first responders) was created under controlled conditions with ethics approval from the ISCTE – Instituto Universitário de Lisboa Ethics Commission (reference CE-ISTA/2025.08, July 2025). The system architecture combines edge-computing computer vision for real-time extraction of facial landmarks, eye state, arm movement, and verbal responses; a clinician-in-the-loop validation layer; and FHIR-mapped Observation resources for direct EHR integration. Three deployment scenarios (Emergency Medical Services, Emergency Departments, and Intermediate Care wards) were designed and compared. Technology adoption was modelled using Rogers’ Innovation Adoption Curve and the Bass Diffusion Model ( p = 0.01, q = 0.35, M = 111 Portuguese hospitals). The architecture achieves low-latency inference with privacy-by-design (local processing, no raw video storage). Stakeholder validation confirmed strong workflow fit and highlighted persistent documentation gaps during EMS-to-hospital transitions. Scenario analysis revealed distinct hardware and integration requirements (ambulance edge device versus ward multi-camera server). Bass modelling projects gradual adoption, reaching approximately 50% of Intermediate Care wards by 2037 in the realistic scenario, with the “chasm” phase occurring between 2030 and 2032. Sensitivity analysis identified early clinical evidence and FHIR integration support as the strongest accelerators of diffusion. As this constitutes a proof-of-concept study, no quantitative AVPU classification metrics (e.g., accuracy, sensitivity, specificity, or confusion matrix) are reported at this stage; empirical model evaluation against expert-annotated clinical recordings is identified as the primary prerequisite for future validation and clinical translation. As a proof-of-concept that has not yet undergone clinical validation, Consc.ia offers a feasible, interoperable solution for standardising AVPU documentation and strengthening early warning systems. By combining video analytics, edge computing, clinician validation, and FHIR integration, the platform addresses a longstanding gap in emergency-care digitalisation and provides a clear roadmap for real-world adoption.
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Elvas, Luìs B. & Carvalho, Carolina
(2026).
Domain-specific versus general large language models: a review and empirical benchmark in real medical texts.
Scientific Reports.
16.
doi:
10.1038/s41598-026-62884-7.
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Abstract The rapid adoption of Large Language Models (LLMs) has led to their widespread use as a general-purpose, fast-deployment solution across a broad range of tasks. In healthcare settings, this trend has accelerated the replacement of task-specific models with large generative architectures, often without an assessment of their efficiency or suitability for structured information extraction. While transformer-based encoder models have long been established as reliable solutions for clinical Named Entity Recognition (NER) and Information Extraction (IE), the increasing reliance on LLMs raises concerns regarding computational cost, scalability, energy consumption, and practical deployment in resource- and privacy-constrained environments. This study aimed to critically evaluate the assumption that LLMs constitute a universally superior solution for clinical NER and IE, by systematically comparing encoder-based Language Models (LMs), and LLMs in terms of extraction performance, efficiency, deployment feasibility, and environmental impact. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 2020) guidelines, a scoping literature review was conducted using Scopus and Web of Science, covering peer-reviewed studies published between 2022 and 2025. In addition, a proof-of-concept case study was performed on European Portuguese clinical notes, comparing a domain-adapted encoder model (MediAlbertina-1.5B) with quantized Llama-based LLMs executed locally under identical hardware conditions. Models were evaluated using micro-averaged precision, recall, and F1-score, together with runtime, estimated energy consumption, and CO₂ emissions. The literature review showed that LLMs and transformer-based architectures were the most frequently used even thought encoder-based and domain-adapted models often perform strongly in clinical NER, particularly when recall, structured output, and computational efficiency are important. The empirical case study showed a similar pattern: MediAlbertina-1.5B achieved the highest performance (micro-F1 = 0.430), whereas all evaluated Llama variants obtained markedly lower F1-scores (≤ 0.123) due to systematic under-extraction. Despite their lower performance, the Llama models incurred 23–95 × longer runtimes and one to two orders of magnitude higher estimated energy consumption and CO₂ emissions under the tested local CPU-only setup. In this proof-of-concept benchmark on 20 European Portuguese clinical reports, the domain-adapted MediAlbertina token-classification model achieved higher recall and micro-F1 than the evaluated locally executed, quantized Llama models. The Llama models were also substantially slower and less sustainable, with higher estimated energy use and CO₂ emissions under the tested hardware and prompting configuration. These findings suggest that domain-adapted encoder models may be preferable for similar structured clinical NER settings, although larger and externally validated benchmarks are needed before generalizing to other languages, clinical corpora, model families, or deployment environments.
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Cuccaro, Francesco; Tapia, Rodrigo; Gatta, Valerio & Marcucci, Edoardo
(2026).
Who Killed the Mobility Hub? Parking Pricing, Access Conditions, and Mode Choice at Rome Trastevere.
Future Transportation.
6(4).
doi:
10.3390/futuretransp6040133.
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Mobility hubs promise to reduce car dependence and make multimodal travel work in practice, yet behavioural evidence remains limited when hub improvements coexist with easier car access. This article examines the tension at Rome Trastevere, an urban rail node that gradually acquires mobility-hub functions while facing improved parking access near Piazza della Radio. The empirical analysis combines a pilot survey of 83 users with an on-site stated preference survey of 204 valid respondents. The stated preference instrument uses a route-based feasible-choice design with nine choice sets per experiment: respondents evaluate alternatives among bikes, walking, e-scooters, e-mopeds, public transport, private cars, and shared cars under variations in travel time, travel cost, and search time. The paper estimates a multinomial logit model in Apollo and uses sample enumeration, supported by Monte Carlo simulation, to assess four parking and shared-mobility scenarios and produce confidence intervals around predicted probabilities. Results show that users respond to time, monetary cost, and search friction in coherent and policy-relevant ways. Setting the car parking search time to zero increases predicted car probability only marginally, by about 0.9% relative to the baseline. By contrast, a EUR 1/h increase in parking cost reduces predicted car probability by about 14.7%, while a EUR 1.5/h increase reduces it by about 22.4%. A coordinated scenario combining higher parking cost and lower shared-mode search time produces the lowest predicted car probability and strengthens e-scooter and e-moped alternatives, while public transport remains the dominant option. Findings indicate that parking pricing steers behaviour more clearly than parking convenience destabilizes it in the tested range. The paper shows that mobility-hub performance depends on coordinated access management, including parking regulation, shared-service reliability, and legible multimodal transfer.
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Kleivenes, Ole Martin & Rekdal, Per Kristian
(2026).
Equal Opportunities at the Net: Geometric Insights Into Volleyball Scaling.
European Journal of Sport Science (EJSS).
ISSN 1746-1391.
26(6).
doi:
10.1002/ejsc.70184.
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This paper investigates how volleyball net height can be scaled to account for differences in vertical spike reach. Although suchdifferences are not exclusively determined by sex, the observed gap between male and female players makes sex‐based com-parisons particularly relevant. The official net heights of 243 cm for men and 224 cm for women differ by 19 cm, a regulationintended to compensate for physical disparities between the sexes. We examine whether this adjustment ensures comparableoffensive opportunities when evaluated in terms of observed spike reach. To isolate the geometric effect, other performance‐related factors are deliberately disregarded. Using a geometric model linking net height to spike reach, our analysis showsthat the current 19 cm gap is insufficient to ensure equal attacking opportunities. At the elite level, geometric equivalence wouldrequire lowering the women's net to about 213 cm or raising the men's net to about 253 cm. The disadvantage faced by femaleplayers becomes even more pronounced at lower performance levels.
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Bakker, Steffen J.S.; Ahmed, Mohamed Ben; Andersson, Henrik; Inngjerdingen, Christian Håkon Torsten & Møller, Simen Aksland
(2026).
A look-ahead policy for dynamic bike rebalancing with neighborhood interactions.
Omega. The International Journal of Management Science.
ISSN 0305-0483.
144.
doi:
10.1016/j.omega.2026.103623.
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Glærum, Marianne & Klakegg, Øystein
(2026).
Tidsbruk i tilsynssaker om dyrevelferd: Analyse av Mattilsynets praksis og rettslige rammer.
I Alteren, Johanne; Hvattum, Lars Magnus; Oskars, Trond Roger; Straume, Solveig & Teryokhin, Sergei (Red.),
Tid. Fjordantologien 2026.
Scandinavian University Press (SCUP).
ISSN 9788294167364.
s. 157–183.
doi:
10.18261/9788294167371-26-08.
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Artikkelen undersøker om dyrevelferden for landbruksdyr har blitt bedre etter Riksrevisjonens kritikk av Mattilsynet i 2019. Studien bygger på analyse av årsrapporter, mediesaker og rettsavgjørelser fra 2019–2024. Resultatene viser at vanskjøtsel fortsatt kan pågå over lang tid, og at tilsynsfrekvensen har gått ned. Kritikken har derfor ikke ført til tydelig forbedring. Hyppigere tilsyn, strengere reaksjoner og økte ressurser til Mattilsynet og Landbruks- og matdepartementet er nødvendig.
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Alteren, Johanne; Hvattum, Lars Magnus; Oskars, Trond Roger; Straume, Solveig & Teryokhin, Sergei
(2026).
Tidens mangfold.
I Alteren, Johanne; Hvattum, Lars Magnus; Oskars, Trond Roger; Straume, Solveig & Teryokhin, Sergei (Red.),
Tid. Fjordantologien 2026.
Scandinavian University Press (SCUP).
ISSN 9788294167364.
s. 13–19.
doi:
10.18261/9788294167371-26-01.
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Phuyal, Sudip; Bhandari, Manila; Bista, Rabindra & Ferreira, Joao C.
(2026).
Blockchain-Based Dynamic and Revocable Consent for Secondary Health Data Use: Systematic Review.
JMIR Medical Informatics.
14.
doi:
10.2196/88536.
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Background: The secondary use of health data holds substantial potential for advancing biomedical research, strengthening population health analytics, and enabling artificial intelligence–driven decision-making support. Yet, ensuring that such reuse respects patient autonomy, privacy, and regulatory obligations remains a major challenge. Conventional consent mechanisms are typically static, difficult to revoke, and offer limited transparency or accountability after data disclosure.
Objective: This review aimed to systematically examine blockchain-based frameworks that enable dynamic, auditable, and revocable consent for the secondary use of health data.
Methods: A structured literature search was conducted in PubMed, Scopus, and Web of Science covering the period 2020 to 2025. Following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, 55 peer-reviewed studies meeting predefined inclusion criteria were analyzed. Data extraction focused on four dimensions: (1) consent life cycle management, (2) auditability and traceability, (3) usability and patient empowerment, and (4) legal and ethical alignment.
Results: Findings indicate that blockchain technologies provide a robust foundation for automating consent life cycles, ensuring immutable auditability, and enabling decentralized patient control. Most frameworks used smart contracts, decentralized identifiers, and verifiable credentials to implement programmable and verifiable consent processes. Nevertheless, key challenges persist, including limited usability testing, complexities in real-time revocation propagation, interoperability gaps with clinical systems, and tensions with regulatory requirements such as the General Data Protection Regulation right to erasure. Only a small subset of studies reported real-world deployments or user-centered evaluations.
Conclusions: Blockchain offers substantial promise for improving the trustworthiness, transparency, and accountability of consent management for secondary health data use. However, wider adoption requires human-centered design approaches, stronger interoperability through standards such as Fast Healthcare Interoperability Resources, verifiable credentials, and consent receipts, and clearer legal guidance for compliance. Future research should prioritize integrating blockchain-enabled consent infrastructures into national and cross-border digital health ecosystems such as the European Health Data Space to support secure, patient-controlled, and ethically governed secondary data use.
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Phuyal, Sudip; Bhandari, Manila; Bezerra, Ricardo Correia; Bista, Rabindra & Ferreira, Joao C.
(2026).
Dynamic Consent for Secondary Use of Health Data: Challenges and Opportunities Under European Law.
JMIR Medical Informatics.
14.
doi:
10.2196/93348.
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Secondary use of health data is essential for advancing medical research, innovation, and public health policy across Europe. Traditional static or broad consent models are increasingly inadequate in complex, multistakeholder digital ecosystems. Dynamic consent, which enables granular, interactive, and ongoing management of individual preferences, including revocation, has emerged as a patient-centered alternative. This integrative review examines the legal feasibility and practical challenges of implementing dynamic consent for secondary health data use under the General Data Protection Regulation (GDPR) and the European Health Data Space (EHDS) Regulation. Drawing on doctrinal legal analysis, European policy documents, national derogations, and technical standards including Health Level Seven Fast Healthcare Interoperability Resources, electronic Identification, Authentication and Trust Services 2.0, European Digital Identity Wallet, and distributed ledger approaches, the study synthesizes legal, governance, and informatics perspectives. Findings indicate that while the GDPR establishes parameters supportive of specific, informed, and revocable consent, significant barriers persist due to national fragmentation, divergent lawful bases for processing, and limited cross-border revocation mechanisms. The EHDS, with provisions phasing in from 2029, shifts governance toward institutional authorization via Health Data Access Bodies and secure processing environments, reducing reliance on individual consent for many large-scale uses. Technical prerequisites, machine-readable consent artifacts, high-assurance digital identity, and policy-based enforcement remain unevenly developed. Nevertheless, integration with data altruism mechanisms under the Data Governance Act and emerging interoperability tools offers promising pathways. A 3-stage operational architecture (consent administration, decision, and enforcement) is proposed to embed dynamic consent within the hybrid EHDS-GDPR framework. However, challenges including blockchain immutability conflicts with the right to erasure, revocation propagation across systems, implementation costs, consent fatigue, and digital divides must be addressed. Dynamic consent cannot serve as a universal solution but can meaningfully enhance transparency and trust when deployed contextually alongside institutional safeguards. Coordinated EU-level harmonization, standardization, and inclusive design will be essential for its successful operationalization.
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Correa, Elder Bruno Evaristo; Ferreira, Joao C. & Abelém, Antonio J. G.
(2026).
Decoupling Governance from Consensus: An Anchor-Only Architecture for EHDS Based on eIDAS 2.0.
IEEE Access.
14,
s. 1–1.
doi:
10.1109/access.2026.3700449.
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The European Health Data Space (EHDS) requires interoperable and secure infrastructures capable of supporting cross-border health data exchange while remaining aligned with the General Data Protection Regulation (GDPR). Existing Distributed Ledger Technology (DLT)-based approaches remain predominantly ledger-centric, coupling governance logic with consensus execution and introducing non-deterministic scalability, high computational overhead, and structural conflicts with GDPR principles such as data minimization and the Right to be Forgotten. This paper proposes the Machine-Readable Governance (MRG) Anchor-Only architecture, a federated and infrastructure-agnostic model that decouples governance semantics from blockchain execution. Leveraging eIDAS 2.0 components—Decentralized Identifiers (DIDs) and Verifiable Credentials (VCs)—the architecture externalizes identity verification, policy evaluation, and purpose-bound access control to decentralized off-chain agents operating at the network edge. The blockchain is reduced to a passive semantic anchoring layer recording only fixed-size 32-byte cryptographic commitments derived from deterministic canonicalization (RFC 8785). A Proof-of-Concept (PoC) was implemented on an 8-node Hyperledger Besu consortium and evaluated under cross-border healthcare conditions. The complete smart contract OrganicGovernanceRegistry.sol, ABI, deployment scripts, workload generator, raw benchmark data, and Besu configuration are publicly available at https://github.com/evaristocpqd/ehds-mrg.git. Experimental results demonstrate a three-order-of- magnitude reduction in cryptographic processing latency relative to a worst-case pure-Solidity on-chain verification baseline (from ∼2500 ms on-chain to ∼2.5 ms off-chain; the choice of baseline and its limitations are discussed explicitly in Section V-A), sub-100 ms DID resolution latency at 2,000 requests/hour, and constant gas consumption of 64,960 units per anchoring transaction independent of governance policy complexity. As a structural consequence of the anchor-only design, the on-chain footprint is fixed at 192 bytes per record (versus >2,560 bytes for full OIDC4VP payloads in ledger-centric baselines), and no Protected Health Information (PHI) or Personally Identifiable Information (PII) is ever recorded on the ledger.We emphasize that all GDPR-related guarantees claimed in this work apply strictly to the blockchain anchoring layer only. No Protected Health Information (PHI) or Personally Identifiable Information (PII) is ever recorded on the ledger. Off-chain agents, the Data Sharing Repository (DSR), and the Verifiable Data Registry (VDR) process personal data and remain fully subject to standard GDPR obligations under Articles 5, 6, 25, and 32, which must be addressed by complementary technical and organizational measures.Within this scoped interpretation, the architecture achieves purpose-bound governance (P1), deterministic on-chain anchoring (P2), and ledger-level data minimization with zero on-chain exposure of PHI/PII (P3).
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Ferreira, Joao C.; Sousa, Carlos Manuel Pereira E; Esperanca, Marco Delgado & Taheri, Babak
(2026).
Forecasting Airport Passenger Flows Using Mobile Network Data: Insights for Sustainable Mobility and Strategic Infrastructure Planning.
Business Strategy and the Environment (BSE).
ISSN 0964-4733.
doi:
10.1002/bse.71058.
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Mata, Pedro Neves; Ferreira, Joao C. & Martins, José Moleiro
(2026).
Agility, Innovation and Risk: A Systematic Literature Review on New Software Product Development and Organisational Capabilities.
Archives of Computational Methods in Engineering.
ISSN 1134-3060.
doi:
10.1007/s11831-026-10606-7.
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Abstract The aim of the study is to synthesise fragmented research streams to understand how organisational capabilities shape software development results such as delivery speed, software quality, innovation performance, and risk mitigation in volatile digital environments. Guided by PRISMA protocol, the review applied dual coding, bibliometric mapping, and narrative synthesis to ensure methodological rigour. Findings reveal three dominant clusters: agile control loops and adaptive routines, which operationalise agility through dashboards, continuous integration, and interactive stand-ups; risk governance and security safeguards, embedding blockchain protocols, DevSecOps, and AI-driven risk detection into agile pipelines; and co-evolution of infrastructures and innovation practices, where cloud-native stacks, digital twins, and collaborative platforms align agility and risk with innovation. Results show that the mere adoption of agile routines is insufficient; their value depends on whether they improve responsiveness, delivery reliability, innovation performance, and controlled risk exposure through knowledge integration, balanced risk governance, and cultural conditions that foster experimentation. The study concludes that new software product development effectiveness derives from the dynamic entanglement of agility, innovation and risk as mutually reinforcing organisational capabilities. Future research should employ longitudinal and multi-level designs to capture how these capabilities co-evolve in digital ecosystems.
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Mata, Pedro Neves; Ferreira, Joao C. & Martins, Jose Moleiro
(2026).
Evaluating the Impact of Software Product Efficiency and Effectiveness on Agile Project Success: A Strategic Technology Management Perspective.
International Journal of Innovation and Technology Management (IJITM).
ISSN 0219-8770.
23(2).
doi:
10.1142/S0219877026500021.
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Kowalska, Aleksandra & Bråthen, Svein
(2026).
Life cycle emissions of electric and internal combustion vehicles in Poland: a comparative assessment with distance intersection point analysis.
Journal of Transport and Sustainability (JTaS).
2(1),
s. 144–166.
doi:
10.1108/jtas-08-2025-0050.
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Purpose
This study examines the life cycle greenhouse gas (GHG) emissions of electric vehicles (EVs) in Poland, where the electricity grid remains one of the most polluting in the European Union (EU).
Design/methodology/approach
Using a life cycle assessment (LCA) approach and a distance intersection point (DIP) calculator, the environmental performance of EVs was compared with internal combustion engine vehicles (ICEVs) in three vehicle classes – small city cars, mid-size cars and SUVs – at different operational periods and annual mileage levels. Coefficients applied in the calculators were derived from literature and publicly available vehicle data. Emissions from the use phase reflect Polish grid decarbonization under the National Energy Policy (PEP2040).
Findings
The results show that while EVs generally have lower life cycle GHG emissions than ICEVs, the advantage depends on vehicle size, lifespan, mileage and electricity mix. Replacing a compact, fuel-efficient ICE with a larger EV can result in higher total emissions, whereas comparing vehicles within the same class – such as medium-sized cars or SUVs – EVs tend to perform better over time. For small city cars, the emission savings are less certain and depend heavily on annual mileage. Over a 16-year lifetime at 15,000 km/year, a mid-size EV emits about 40% less GHGs than an equivalent ICEV under PEP2040. Its DIP would be 114,000 km, decreasing to 73,000 km under an EU-aligned grid. Break-even distance decreases across all classes and the difference between PEP2040 and EU-aligned grid diminishes with vehicle size, due to higher battery production emissions.
Originality/value
EV sustainability results appear to be country specific and car size dependent.
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Hovde, Markus Grande; Elde, Mari Elise; Aandal, Line & Wen, James Charles
(2026).
Beyond Student Polling: Creating Classroom Tools that Help Teachers Respond.
I Krems, Josef F.; Silva, Hugo Plácido da & Cipresso, Pietro (Red.),
Computer-Human Interaction Research and Applications: 9th International Conference, CHIRA 2025, Marbella, Spain, October 20–21, 2025, Proceedings, Part I.
Springer.
ISSN 9783032164483.
s. 379–388.
doi:
10.1007/978-3-032-16448-3_21.
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Student Response Systems have long promised to enhance classroom engagement, yet adoption across higher education remains modest. To explore barriers to uptake, we interviewed teachers who have used such technology in their classes. Their experiences revealed three key challenges: steep learning curves, rigid constraints from pre-scripted quizzes, and disruptions to natural lecture flow. Drawing on Human-Computer Interaction principles, we translated these insights into design guidelines that emphasize learnability, flexibility, and minimal disruption. We developed a prototype Teacher Response System that enables students to signal confusion or pose questions anonymously, offering teachers low-friction, real-time feedback. This work positions a complementary approach that combines contrasting response systems to bridge structured student-centric interaction with dynamic, teacher-led responsiveness to support the situated practices of classroom teaching.
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Juma, Alexandre; Elvas, Luìs B.; Ferreira, Joao C. & Nunes, Luis
(2026).
Mobility effect on city pollution: A case study.
Journal of Ambient Intelligence and Smart Environments.
ISSN 1876-1364.
18(2),
s. 234–248.
doi:
10.1177/18761364251396640.
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The present work reports the impacts on urban mobility and air quality in Lisbon, Portugal, of the imposed restrictions to curb the transmission of SARS-CoV-2 virus, which causes COVID-19 disease. We performed a data-driven approach over Lisbon Smart cities data, collected from several sources, such as traffic and pollution. During the first Portuguese emergency period (18-03-2020 to 03-05-2020) the sharp reductions in anthropogenic activities, most importantly road traffic, resulted in generally reduced criteria air pollutant concentration compared to an homologous baseline from 2013–2019 measured in the six air quality monitoring stations throughout the city. The most negatively impacted air pollutants were NO2, with a reduction of 54.35% in traffic stations and 28.62% in background stations. Google mobility indicator for local commerce was found to be the main anthropogenic activity indicator for Lisbon, with a moderate and positive correlation with NO2 concentration (r=+0.54). A regressor ML pipeline was trained to predict NO2 concentration with the available anthropogenic activity, weather, and air pollutant inputs from March/2020 to March/2021, achieving R2 = 0.925 on the test set.
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Mishra, Alok; Alzoubi, Yehia Ibrahim; Iftikhar, Muhammad Hassan & Khan, Amama Bushra Jahangir
(2026).
Empirical insights into supply chain management integration with product lifecycle management: benefits and challenges.
Engineering Management in Production and Services.
ISSN 2543-6597.
18(1),
s. 87–106.
doi:
10.2478/emj-2026-0007.
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Integration of product lifecycle management systems with supply chain management processes has proven challenging for companies that attempt it, with limited empirical research available on this topic. Consequently, this study aimed to explore whether implementing product lifecycle management systems can enhance supply chain management within an organisation. To obtain relevant data, this study employed a single qualitative case study design, using in-depth semi-structured interviews and on-site observations conducted at ABC TECH to examine how Sovelia PLM supports supply chain processes. The data was collected from a company in Northern Europe that uses Sovelia lifecycle management software. Findings showed that this software generally aids in enhancing supply chain process integration, traceability, and structure. However, it is design-centric, which makes it more difficult to adequately address the supply chain management department’s operational requirements. These insights may enhance the decision-making processes and guide actionable strategies for seamless integration. The findings call for a more holistic product lifecycle management system that encapsulates both design and supply chain functionalities, thereby ensuring cohesive operations and improved communication within the company’s supply chain network.
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Kulkarni, Akshay; Upadhyay, Amit; Mangla, Monika & Aggarwal, Swati
(2026).
Ensemble CNN for colon cancer detection using histopathological image.
Frontiers in Oncology.
16.
doi:
10.3389/fonc.2026.1674606.
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Colon cancer remains one of the leading causes of cancer-related mortality worldwide, with delayed detection significantly impacting survival rates. The time-consuming nature of traditional histopathological analysis contributes to these diagnostic delays. This study leverages advances in artificial intelligence to improve both the speed and accuracy of colon cancer detection through automated analysis of tissue sample images. We propose a weighted ensemble model comprising four convolutional neural networks namely RegNet X, RegNet Y, Swin B, and VGG11, that enhances prediction accuracy by combining outputs from base models. The prime focus of the research is to devise a recall prioritized ensemble model capable of detecting various types of colon cancers, while also examining the possibility of the model to generalize beyond the training domain. The ensemble was trained using a dataset of 10,000 histopathological images of colon tissues containing both benign and malignant samples. We introduce a novel preprocessing pipeline incorporating histogram equalization and contrast stretching for image enhancement with a motive to improve diagnostic accuracy. Notably, the ensemble model demonstrated superior recall metrics compared to traditional ML approaches—a critical measurement in healthcare applications. The ensemble model proposed has a test accuracy of 99% and a recall of almost 100%. Further, the proposed model is also tested for lung cancer to validate its generalizability. The experimental results obtained establish the validation of proposed model in terms of generalizability, demonstrating strong performance.
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Gergis, Faris Henry & Begovic, Marko
(2026).
Exploring exchange and influence legitimacy in sustainable urban mobility schemes: a case study of Kristiansund’s city package.
International Journal of Urban Sustainable Development.
ISSN 1946-3138.
18(1),
s. 69–91.
doi:
10.1080/19463138.2026.2660516.
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This paper explores the exchange and influence legitimacy in Kristiansund’s City Package. While much of the existing literature focuses on larger Norwegian cities, smaller cities with unique contextual challenges remain underexplored, complicating the transfer of urban strategies from larger contexts. Findings from analysing the content of official documents, media articles, and interviews revealed that decision-making processes were predominantly top-down, with the regulatory and incentivising measures of exchange legitimacy unequally distributed in terms of proximity to incentives. Influence legitimacy was primarily concentrated among politicians and bureaucrats, with public inclusion limited to post hoc endorsement of policies adopted elsewhere.
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Ferreira, Joao C.; Correia, Ricardo & Rosário, Luís
(2026).
Blockchain Integration for Trust and Consent Management in MCP-Enabled FHIR Systems.
I Unknown (Red.),
2025 IEEE International Conference on Distributed Ledger Technologies (ICDLT).
IEEE (Institute of Electrical and Electronics Engineers).
ISSN 9798331555672.
doi:
10.1109/icdlt66400.2025.11466653.
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The Model Context Protocol (MCP) enables LLMmediated querying and synthesis across distributed FHIR servers but lacks builtin guarantees for provenance, tamperproof auditing, and granular patient consent. We present a trust layer that integrates permissioned blockchain smart contracts, verifiable credentials (VCs), and decentralized identifiers (DIDs) with MCPenabled FHIR workflows. Our architecture immutably logs MCP queries and hashes of returned FHIR bundles, enforces patientcentric consent via onchain policies, and returnsss verifiable audit trails alongside narrative summaries. We detail contract interfaces, threat model, and deployment options (L2/sidechains; ZK attestations). Using Synthea derived cohorts, we benchmark endtoend latency, throughput, consentrevocation latency, and audit verifiability against three baselines (OAuth2/UMA, PKI audit logs, and TEE attestations). Results show comparable latency (p95<250 ms added), deterministic audit recovery (100 %), and materially faster consent revocation propagation (p501.8 s) with modest cost (<1 per 1k queries on L2). We discuss risks from LLM indeterminism and introduce grounding, constrained decoding, and abstention policies. The approach advances interoperable, patientcontrolled AI summarization with verifiable trust and practical deployment pathways.
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Esperança, Marco; Correia, Ricardo & Ferreira, Joao C.
(2026).
Blockchain-Based System for Monitoring Vaccine Transportation with Temperature Sensing.
I Unknown (Red.),
2025 IEEE International Conference on Distributed Ledger Technologies (ICDLT).
IEEE (Institute of Electrical and Electronics Engineers).
ISSN 9798331555672.
doi:
10.1109/icdlt66400.2025.11466720.
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Vaccines are highly temperature-sensitive biological products that require strict cold chain management to preserve their efficacy. Any deviation from prescribed temperature ranges during transportation can lead to irreversible degradation, increasing public health risks and financial losses. Existing vaccine logistics systems face significant challenges in ensuring end-to-end visibility, detecting tampering, and responding promptly to temperature breaches. This paper proposes a permissioned blockchain–based architecture integrated with Internet of Things (IoT) temperature sensors and smart contracts to enable immutable, real-time monitoring of vaccine transportation. The architecture features four functional layers: (1) a sensing layer for continuous temperature, humidity, vibration, and location tracking; (2) a data integration layer for secure aggregation and hashing of sensor data; (3) a blockchain layer for tamper-proof recording and compliance enforcement; and (4) an application layer providing stakeholders with dashboards, alerts, and audit capabilities. Smart contracts automatically validate environmental data against vaccine-specific thresholds and trigger alerts or custody holds upon detecting non-compliance. By prioritizing temperature sensing as a critical driver of decision-making, the proposed system enhances vaccine safety, strengthens supply chain transparency, and reduces spoilage. Potential applications include national immunization programs, global vaccine distribution campaigns, and emergency health interventions.
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Pereira, Rui; Boavida, Gonçalo & Ferreira, Joao C.
(2026).
Blockchain to Ensure Transparency in Lobbying.
I Unknown (Red.),
2025 IEEE International Conference on Distributed Ledger Technologies (ICDLT).
IEEE (Institute of Electrical and Electronics Engineers).
ISSN 9798331555672.
doi:
10.1109/icdlt66400.2025.11467076.
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Transparency and accountability in lobbying and public affairs are critical to fostering public trust and safeguarding democratic decision-making. Existing transparency registers often face limitations in interoperability, auditability, and data integrity, creating opportunities for incomplete disclosures or manipulation. This paper proposes a permissioned blockchain architecture for the secure and verifiable registration of lobbying interactions between public officials and private entities. The proposed system leverages Hyperledger Fabric due to its modularity, support for private channels, and granular permission management, ensuring compliance with legal and institutional requirements. A comparative analysis of leading permissioned blockchain platforms—Hyperledger Fabric, Quorum, and Corda—supports the technological choice. The architecture integrates privacy-by-design principles, robust encryption, and Byzantine Fault Tolerant consensus to provide immutable, auditable, and privacy-preserving records. This work contributes a novel GovTech approach that combines blockchain-based immutability with tailored access control to strengthen institutional transparency. Although primarily conceptual, the proposed architecture is validated through a scenario-based analysis illustrating its applicability to the European Union Transparency Register and national lobbying registers.
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Almeida, Paulo & Ferreira, Joao C.
(2026).
A Blockchain Architecture for Wood Traceability: A Case Study in the Pulp and Paper Industry.
I Unknown (Red.),
2025 IEEE International Conference on Distributed Ledger Technologies (ICDLT).
IEEE (Institute of Electrical and Electronics Engineers).
ISSN 9798331555672.
doi:
10.1109/icdlt66400.2025.11467098.
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The EU Deforestation Regulation (EUDR) requires geolocated due-diligence evidence and auditable traceability across complex, multi-party wood supply chains. We present conceptual prototype VeriWood, a permissioned, Ethereum-compatible blockchain architecture (PoA consensus) designed for the pulp and paper industry. Using Design Science Research, we derive a threat model—double counting of certified wood, log back-dating/erasure, supplier mis-reporting, GPS tampering, and certificate spoofing—and motivate blockchain over a centralized, signed-log database: multi-party consensus and immutable, replicated ledgers mitigate unilateral alteration and enable non-repudiation among organizations with misaligned incentives. The architecture links geospatial polygons and EUDR TRACES identifiers to production batches and mass-balance transitions (from pulp digester to paper machines and finished goods). Data-minimization is enforced by hashing evidence on-chain, while off-chain stores and selective disclosure address GDPR. IoT oracles are anchored through signed feeds with hardware-rooted attestation and anomaly-detection hooks; verifiable credentials support issuance and revocation of compliance claims. We implement a Proof of Concept (PoC), traceability graph, and audit tables, and walk through a real industrial process map from a leading EU producer. The results show end-to-end provenance continuity and auditable due-diligence events, reducing manual reconciliation and enabling tamper-evident certification audits. This work contributes (i) a sector-specific threat model and PoA justification, (ii) an architecture that operationalizes EUDR/TRACES downstream into manufacturing, and (iii) a PoC demonstrating feasibility and integration pathways.
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Čale, Daniel; Ferreira, Joao C.; Madureira, Ana Maria & Coutinho, Carlos
(2026).
Leveraging Blockchain Integrity Mechanisms and IoT Sensors to Boost Internal Process Efficiency in Logistics Management.
I Unknown (Red.),
2025 IEEE International Conference on Distributed Ledger Technologies (ICDLT).
IEEE (Institute of Electrical and Electronics Engineers).
ISSN 9798331555672.
doi:
10.1109/icdlt66400.2025.11466647.
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Fleet logistics management requires reliable monitoring of temperature-sensitive goods and asset utilization to meet regulatory requirements and operational efficiency targets. This paper presents an integrated framework combining blockchain technology and IoT sensors to enhance internal process efficiency and data integrity in logistics operations. The research develops and deploys a permissioned blockchain system within a Portuguese logistics company, enabling empirical evaluation of core performance metrics including end-to-end latency, transaction throughput, and audit traceability under operational conditions. A pilot study involving 12 sensors distributed across transport operations demonstrates measurable improvements in audit preparation efficiency. Analysis indicates that low-latency event registration (meaning 5 seconds) supports operational monitoring requirements, whilst automated evidence generation with cryptographic proofs reduces manual verification overhead in internal and external audit processes. The study establishes performance benchmarks and cost-benefit analysis comparing blockchain adoption against centralized logging solutions with digital signatures. The architecture enhances decision-making transparency by providing logistics managers with cryptographically verifiable operational data, whilst governance insights support organizations implementing blockchain-based integrity mechanisms in regulated environments.
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Curado, Joel; Martins, Ana L & Ferreira, Joao C.
(2026).
Blockchain Technologies for Traceability and Efficiency in Service-Sector Supply Chains.
I Unknown (Red.),
2025 IEEE International Conference on Distributed Ledger Technologies (ICDLT).
IEEE (Institute of Electrical and Electronics Engineers).
ISSN 9798331555672.
doi:
10.1109/icdlt66400.2025.11467106.
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Liquids-focused service-sector supply chains (healthcare cold chain, maritime documentation, luxury spirits) face concurrent challenges in traceability, privacy, and throughput. We present a modular blockchain architecture that combines a permissioned core with zeroknowledge proofs (zk-SNARKs) for selective disclosure, Layer-2 batching for scale, and IoT edge ingestion for realtime monitoring. Using Design Science Research, we design, prototype, and simulate representative workloads. Under WAN latencies typical of port operations, L2 batching reduces end-to-end verification latency relative to a Fabriconly baseline; in cold-chain scenarios, proof cadence tuning maintains privacy with bounded overhead. We discuss oracle and interoperability risks and provide a cost model. Results and artifacts are intended to guide practitioners balancing privacy, performance, and compliance in regulated logistics.
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Batista, Bruno L. A.; Correia, Ricardo; Ferreira, Joao C. & Júnior, Joaquim Celestino
(2026).
AegisChain: Blockchain-Mediated Access Control for Privacy-Preserving DICOM Interoperability.
I Unknown (Red.),
2025 IEEE International Conference on Distributed Ledger Technologies (ICDLT).
IEEE (Institute of Electrical and Electronics Engineers).
ISSN 9798331555672.
doi:
10.1109/icdlt66400.2025.11466719.
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Medical imaging is a critical component of modern diagnostics, yet interoperability and secure access control across institutions remain major challenges. This paper introduces AegisChain, a blockchain-based framework designed to facilitate privacy-preserving interoperability of DICOM images across healthcare institutions. Conceptually building upon the Ancile architecture, AegisChain is adapted for Hyperledger Fabric and designed around what we term the “zero-PHI-on-chain” principle, a security model wherein no Protected Health Information is ever stored on the blockchain. Instead, the ledger functions as a decentralized Access Control List (ACL) and discovery service. It employs a streamlined three-contract model (Ownership, Consent, and Logging) to manage non-sensitive pointers and enforce patient-centric access policies. We demonstrate the framework's feasibility and performance through a proof-of-concept implementation. Our micro-benchmarks show that on-chain access control checks introduce negligible overhead, with a median read latency of less than 1 ms and a concurrent throughput exceeding 13,000 queries per second. Through its novel, simplified, and performant architecture, AegisChain addresses key barriers in cross-institutional imaging workflows, laying a foundation for a more collaborative and patient-centered healthcare ecosystem.
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Konovalenko, Anna; Hvattum, Lars Magnus & Iversen, Kim Aleksander Hammer
(2026).
Data describing planned routes and driven routes from a last-mile delivery vehicle routing problem.
Data in Brief.
66(June).
doi:
10.1016/j.dib.2026.112744.
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This dataset provides real-world last-mile delivery routes from a logistics company, offering valuable resources for research in route optimization and logistics. The dataset addresses a substantial gap between theoretical route planning and routing performed by drivers who often do not follow the proposed routes. The dataset comprises 19,497 pairs of routes, including both planned routes, generated by human dispatchers employing heuristic optimization tools, and the routes that the drivers eventually followed in practice. The routes were completed by 400 unique drivers, covering 10,864 unique locations. In the dataset, the routes are presented as sequences of stops, where each stop is a separate row in the presented datafile. The dataset includes detailed information on routes, stops and drivers’ identifiers, timing, and distance measurements. It allows for a deeper analysis of driver behavior patterns, factors of route deviations, and their impact on delivery performance.
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Odeck, James Oyugi
(2026).
Beyond the Narrative of Failure: Road Project Cost Overruns Are Manageable.
Project Leadership and Society (PLS).
7(December).
doi:
10.1016/j.plas.2026.100222.
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Cost overruns in road transport projects have long been portrayed as inevitable. They were typically measured as the percentage difference between actual and estimated costs at the time of decision-to-build, expressed relative to the estimated cost. Influential studies, notably Flyvbjerg et al. (2002), popularized the view that projects almost always overrun and by large margins. Yet recent evidence tells a different story. Norwegian studies show that overruns have been low and stable over time and declined further after governance reforms such as the Quality Assurance scheme. Much of the escalation occurs during front-end planning, while estimates at decision-to-build are generally accurate. Similar patterns appear elsewhere. New measurement approaches reveal that earlier studies in England and Sweden overstated overruns. In Sweden, estimates stabilize once construction begins. Swiss highway projects show median overruns of around 5%, and Australian and New Zealand studies also find modest medians once outliers are accounted for. Other research highlights the role of reforms in planning, assurance, procurement, and accountability in improving delivery. This essay argues that overruns are not destiny. With the right governance and institutional reforms, governments can act to improve cost performance in road projects. Early studies relied on measurement that overstated central tendencies. Whereas we reflect on the wider and supportive international literature, we focus more on evidence from the Norwegian context.
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Čale, Daniel; Ferreira, Joao C.; Madureira, Ana & Coutinho, Carlos
(2026).
A systematic literature review on Web3 applications in trucking logistics: Impacts and emerging trends in logistics 5.0.
Digital Business.
6(2).
doi:
10.1016/j.digbus.2026.100170.
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Web3 technologies, representing the next generation of a decentralised and user-centric Internet, offer innovative solutions to enhance adaptability, sustainability, and resilience in logistics systems aligned with the principles of Logistics 5.0. This study conducts a Systematic Literature Review (SLR) following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology, analysing peer-reviewed journal articles published between 2018 and 2024 and retrieved from Scopus, Web of Science Core Collection, IEEE Xplore, and ACM Digital Library. The review specifically focuses on trucking logistics, a sector characterised by high fossil-fuel dependency, operational fragmentation, and significant environmental impact. The findings reveal that Artificial Intelligence and Internet of Things technologies dominate current implementations, mainly supporting fleet management, route optimisation, accident prevention, and risk assessment. In contrast, blockchain applications remain limited, and metaverse-based solutions are largely exploratory and confined to training scenarios. Key research gaps include the scarcity of integrated Web3 solutions, the limited consideration of human-centric Logistics 5.0 dimensions, and the lack of large-scale empirical validation in real-world trucking operations. Based on the analysis, this paper proposes a conceptual framework that maps Web3 technologies to trucking logistics areas, investment priorities, and Logistics 5.0 objectives, offering actionable guidance for Logistics Service Providers transitioning from Logistics 4.0 to Logistics 5.0.
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Mikhaylova, Daria; Turchi, Tommaso & Malizia, Alessio
(2026).
How Do We Measure Over-Reliance? A Unified Probabilistic View.
I Kuflik, Tsvi; Kleanthous, Styliani; Chen, Li; Jacucci, Giulio & Renner, Alison (Red.),
IUI '26 Companion: Companion Proceedings of the 31st International Conference on Intelligent User Interfaces.
Association for Computing Machinery (ACM).
ISSN 9798400719851.
s. 66–69.
doi:
10.1145/3742414.3794719.
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Pathak, Mayank; Mishra, Kamta Nath; Singh, Satya Prakash & Mishra, Alok
(2026).
A hybrid machine learning and cryptography-based predictive probability model for enhancing security and privacy in cloud-IoT environment.
Computers & Security.
ISSN 0167-4048.
166.
doi:
10.1016/j.cose.2026.104870.
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Marcucci, Edoardo; Gatta, Valerio; Ciucci, Cristiano & Cucina, Domenico
(2026).
When launch costs fall, do satellites shrink and constellations grow? The role of SpaceX in the SmallSat revolution.
Research in Transportation Business and Management (RTBM).
ISSN 2210-5395.
67(June).
doi:
10.1016/j.rtbm.2026.101663.
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The rapid growth of small satellites has transformed the structure of the global space sector, yet the extent to which declining launch costs have contributed to this transition remains underinvestigated. This paper examines the relationship between launch affordability and the proliferation of satellites with a mass below 500 kg using a comprehensive dataset covering all operational satellites launched between 2003 and 2022. Six linear regression models are estimated to assess whether reductions in average launch costs are associated with changes in payload composition and average satellite mass. Results indicate a strong statistical association between lower launch costs and both an increasing share of small satellites and a substantial decline in average satellite mass after 2013. When low-cost, high-cadence launch services are excluded from the analysis, the relationship weakens, indicating that recent changes in launch supply conditions have played a major role in shaping deployment patterns. Evidence suggests that declining launch costs significantly contributed to the transition toward a small satellite-dominated orbital environment, while also interacting with broader technological, organizational, and market developments. These results shed empirical light into how access-to-orbit economics influences system design choices, investment strategies, and competitive dynamics in the contemporary space economy.
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Yadegari, Mahshid; Urrutia, Sebastián; Santos, Andréa Cynthia & Dang, Quockhanh
(2026).
Integer programming models for unidirectional and bidirectional cyclic load-dependent routing problems.
Journal of Modelling in Management.
ISSN 1746-5664.
s. 1–23.
doi:
10.1108/jm2-09-2025-0468.
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Purpose Vehicle load is a critical factor that impacts route selection within logistic networks. This study aims to compare two different strategies for generating routes in a cyclic load-dependent traveling salesman problem. Design/methodology/approach The study introduces novel load-dependent cyclic routing strategies and propose two new problems to model them: The unidirectional and bidirectional variants of the cyclic load-dependent traveling salesman problem. Three mathematical formulations are proposed for the problems using integer linear programming. Two formulations, for both variants of the problem, use the so-called MTZ constraints while the third one for the bidirectional variant is based on an arc-network. To evaluate the performance of the proposed strategies, the study uses three sets of instances: benchmarks from the literature, realistic instances and randomly generated instances. Findings The study shows that the bidirectional strategy is strictly better than the unidirectional one in instances with unitary distances and demands. For general-distance and general-demand instances, computational results show that the bidirectional strategy obtains better solutions than the unidirectional one in most cases. Originality/value The proposed problems are original despite being variants of cyclic inventory routing problems. The load on the vehicle is often neglected in operational research literature. The bidirectional strategy in the context of cyclic problems is original. While not extremely complex, the theoretical results on unitary distance and unitary demand instances are new.
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Konovalenko, Anna; Hvattum, Lars Magnus & Urrutia, Sebastián
(2026).
Two paradigms for combining optimization and satisfiability : Maximum satisfiability and optimum satisfiability problems.
Journal of Information and Optimization Sciences.
ISSN 0252-2667.
47(2),
s. 399–427.
doi:
10.47974/jios-1392.
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Maximum satisfiability (MaxSAT) and optimum satisfiability (OptSAT) problems are two optimization versions of the NP-complete Boolean satisfiability problem. In the literature, these versions have been described and tackled separately by using specialized solvers for each problem. This paper investigates a connection between MaxSAT and OptSAT where each problem can be reduced to the other. We consider several heuristic solvers and different sets of benchmark instances for each problem and investigate whether one class of solvers can tackle instances of the other problem with competitive results. Through computational experiments, we conclude that specialized solvers for one of the problems cannot compete with specialized solvers for the other problem and point out potential improvements of the solvers that are needed to successfully address a wider range of problem instances.
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Holmgren, Johan & Hansson, Lisa
(2026).
Long-run trends in usage, service levels, and governance of public transport in Sweden.
Research in Transportation Economics.
ISSN 0739-8859.
117(June).
doi:
10.1016/j.retrec.2026.101758.
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An efficient public transport system providing good service to the public is a key element of sustainable development. The aim of this paper is to describe and explain the long-run development of public transport supply variables in Sweden. The analysis uses data on the supply of vehicle kilometres and fare levels, as well as on the development of the cost of providing services. This is seen in light of structural changes, with regression analysis being used to explain the development of the aforementioned variables and important policy changes. The analysis utilises yearly data from Swedish counties from 1986 to 2022, totalling 756 observations. The findings show a cost increase associated with the policy changes made in 1995 (related to Sweden becoming a member of the EU) and a cost decrease associated with changes made in 2012 (related to a new public transport act). The supply of vehicle kilometres has been positively affected by changes introduced in 1995. Fare levels were reduced due to changes introduced in 1989, when public transport authorities were given the opportunity to use competitive tendering. To our knowledge, this is the first longer comprehensive study to have considered more recent reforms and service changes in the Swedish public transport sector.
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Phuyal, Sudip; Bhandari, Manila; Bista, Rabindra & Ferreira, Joao C.
(2026).
HL7 FHIR consent for healthcare data sharing: challenges, opportunities and integrity implications.
International Journal of Medical Informatics.
ISSN 1386-5056.
214.
doi:
10.1016/j.ijmedinf.2026.106405.
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Objective
To assess whether HL7 FHIR Consent, as currently specified and deployed, is sufficient to support verifiable, regulation-aligned consent governance in distributed and cross-organisational health data sharing.
Methods
We conducted a qualitative critical analysis of FHIR Consent informed by (i) peer-reviewed implementation literature, (ii) national-scale consent exchange initiatives, and (iii) accountability requirements under GDPR and the European Health Data Space (EHDS). The analysis is organized into four dimensions: semantic interpretability, consent lifecycle management, runtime enforcement, and cross-organisational trust/auditability.
Results
FHIR Consent provides an interoperable representation of authorisation intent, but large-scale deployments remain limited by (1) non-canonical semantics across implementations, (2) lack of standardized lifecycle versioning and cross-organisational revocation propagation, (3) heterogeneous translation of declarative consent into enforceable access control, and (4) limited capability for independent verification of consent provenance and historical integrity across institutional boundaries.
Conclusion
We derive an architecture pattern that separates (a) standards-based consent representation (FHIR Consent), (b) local policy interpretation/enforcement, and (c) cross-organisational integrity verification. Cryptographic integrity anchoring is discussed as a complementary mechanism for tamper-evident verification of off-chain consent artifacts and lifecycle events, without externalizing consent semantics or personal data.
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Sakita, Benjamin Mosses
(2026).
Technological–inter-organizational–organizational–environmental (TIOE) framework for digital transformation of a maritime port in a developing country.
Research in Transportation Business and Management (RTBM).
ISSN 2210-5395.
67(June).
doi:
10.1016/j.rtbm.2026.101658.
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Digital transformation (DT) has become a strategic imperative in the maritime port sector driven by the need for efficiency, transparency, resilience, and competitiveness in global supply chains. While previous studies have examined DT in ports primarily through the technological–organizational–environmental (TOE) lens, the framework does not explicitly account for the inter-organizational relationships that are foundational to port DT. In highly networked port ecosystems, successful DT depends on collaboration, interoperability, and trust among multiple autonomous actors. To address this conceptual gap, this study extends the traditional TOE model by incorporating an inter-organizational (I) dimension, resulting in a technological–inter-organizational–organizational–environmental (TIOE) framework that provides a more comprehensive analytical lens for examining DT adoption in ports. Using a single embedded case study and multi-method data collection, this research identifies key determinants of DT success at the Port of Dar es Salaam in Tanzania including regulatory adaptability, stakeholder collaboration, environmental pressures, government intervention, and the strategic alignment of digital investments with maritime supply chain integration. The findings reveal that DT in ports is shaped not only by internal organizational capabilities but also by inter-organizational collaboration, environmental dynamics, and regulatory flexibility. This study advances DT scholarship by addressing the overlooked inter-organizational dimension of port digitalization and by offering actionable insights for policymakers, port authorities, and industry practitioners seeking to enhance digital resilience in the maritime domain through a five-stage DT roadmap.
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Esperanca, Marco Delgado; Galvão, Tiago; Freitas, Diogo; Ferreira, Joao C.; Cysneiros, Ana & Bento, Luís
(2026).
Integrating medical imaging datasets with blockchain wallets: A case study on ARDS-COVID19 patients.
PLOS ONE.
21(3).
doi:
10.1371/journal.pone.0338897.
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Medical imaging plays a critical role in diagnosing and managing acute conditions such as Acute Respiratory Distress Syndrome (ARDS), particularly in intensive care settings. However, radiological data are often siloed across Picture Archiving and Communication Systems (PACS), with limited interoperability, traceability, and patient control. This paper proposes and validates a blockchain-enabled architecture that integrates radiological imaging data into a patient-controlled digital wallet (BioWallet) ecosystem. The system combines verifiable credentials, decentralized identifiers (DIDs), and a FHIR-compliant backend to ensure secure, auditable, and standards-based access to DICOM images and associated metadata. The ledger stores only consent and audit hashes, while clinical data remain off-chain and correctable within FHIR/EHR systems, ensuring auditability without hindering rectification. A validation scenario replicating an ICU emergency was conducted using synthetic ARDS-COVID19 cases to assess latency, consent enforceability, and user experience. Results showed a 67% reduction in image access time compared to traditional systems, 100% success in blocking unauthorized access, and high clinician satisfaction. The architecture supports FAIR-compliant reuse of annotated imaging datasets, enhances transparency in image-driven research, and aligns with GDPR and future European digital identity frameworks. This work demonstrates the feasibility and value of a decentralized, patient-centric approach to imaging data governance in high-stakes clinical environments.
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Iannaccone, Gabriele; Gatta, Valerio & Marcucci, Edoardo
(2026).
Policy prioritization in participatory planning: which approach to use, when and why?
Transport Policy.
ISSN 0967-070X.
182(July).
doi:
10.1016/j.tranpol.2026.104090.
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This paper addresses the critical challenge of policy prioritization within Urban Freight participatory planning. While the objectives and activities of such planning are generally defined, clear methodological guidance on selecting the most suitable approach, contingent on specific contextual factors, remains limited. This research systematically classifies and compares alternative approaches against key contextual factors. Jointly considering a comprehensive systematic scientific/grey literature review (64 articles and 11 items, respectively) and scenario analysis allows the study to evaluate both strengths and weaknesses of each approach across various hypothetical planning contexts. Results indicate that, while there is no best solution, Discrete Choice Models generally represent the most suitable approach across diverse scenarios, particularly when dealing with high stakeholder heterogeneity and controversial, monetary-focused policies. Conversely, Discuss and Deliberate methods, while effective for high complexity, are often time-consuming and less suitable when highly heterogeneous groups are key. This paper provides policymakers and practitioners with evidence-based recommendations for selecting context-appropriate participatory methodologies, thereby enhancing the efficiency and legitimacy of UFT policy design while mitigating the risks of delayed or blocked policymaking processes.
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Baral, Sulav; Bista, Rabindra; Karki, Subindra & Ferreira, Joao C.
(2026).
Boundary Aligned Superpixel Guided Dual Stream Vision Transformer for Pneumonia Detection in Chest CT and X-ray.
IEEE Access.
14,
s. 35633–35650.
doi:
10.1109/access.2026.3670098.
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Pneumonia continues to be a significant cause of death around the globe, and quick and accurate diagnosis based on chest X-rays (CXR) and computed tomography (CT) images is of utmost importance. While transformer-based models that process CXR and CT images have been developed, models often make use of fixed patch tokenization, which can lead to a loss of anatomical boundaries and reduce interpretability from a clinical perspective. We fill this gap with a boundary-aware dual-stream vision transformer to leverage both pixel-level detail for images and region-level superpixel representations to make the architecture adaptable to both modalities. The framework was validated on two publicly available datasets (Kaggle) and one dataset from Dhulikhel Hospital, with accuracies on Kaggle CXR = 98.21%; Dhulikhel CXR = 98.74%; Kaggle CT = 99.73%; and Dhulikhel CT = 97.3%. Furthermore, per-class precision, recall, and F1-scores all exceeded 96%, and all ROC–AUC values exceeded 0.99. In addition to more typical metrics, boundary-aware evaluation revealed good alignment with clinicians with edge-alignment cosine scores ranging from 0.27 to 0.34 for CXR and 0.48 to 0.49 for CT and intra-region variance below 0.015, while observing focused attention distributions as evidenced by Gini coefficients ≈0.58 for CXR, and ≈0.33 for CT. Statistical robustness was also confirmed with 95% bootstrap confidence intervals, all reaching significance over single-stream baselines, and particularly emphasizing reduction in false negatives of pneumonia positive cases. These results show that the proposed approach fills the important gap between high diagnostic accuracy and preserving anatomical boundaries, improving clinical trust and understanding in pneumonia detection.
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Werra, Dominique de; Urrutia, Sebastián & Assunção, Lucas
(2026).
Break minimization in incomplete round-robin tournaments.
Discrete Applied Mathematics.
ISSN 0166-218X.
387(July),
s. 6–14.
doi:
10.1016/j.dam.2026.02.023.
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Naghshineh, Bardia & Røstbakken, Anders Walløe
(2026).
The implications of additive manufacturing for hospital supply chain operations: an exploratory study.
Supply chain management.
ISSN 1359-8546.
s. 1–13.
doi:
10.1108/SCM-03-2025-0193.
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Klakegg, Øystein & Sørum, Henning
(2026).
Natural cold freshwater temperatures and light regimes in Atlantic salmon smolt production can improve the sea phase performance.
Veterinary and Animal Science.
32(June 2026).
doi:
10.1016/j.vas.2026.100579.
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A total of 5.4 million smolts of Atlantic salmon (Salmo salar) older than 15 months, were divided into 18 groups and followed after sea-launch. They were all produced in the freshwater stage by employing natural conditions including water temperature and light program. The smolt were reared at water temperatures that in winter is as low as +3 °C and is referred to as “cold smolt”. The 18 groups were transferred to 11 net pen locations in Norway. Mortality, Feed Conversion Ratio (FCR) and share superior of the 18 groups were analyzed after harvesting and compared with the average in the respective production regions (POs) where the fish had been located. When the “cold smolt” had been co-located with the conventionally produced smolt groups, the “cold smolt” where also compared with these.
The results indicate that the "cold smolt" performed better in the sea phase than both the average of all smolt in the respective production areas and compared to other smolts released at the same time at the same locations. Compared to the annual average in the same PO the “cold smolt” performed, from 2 % to 74 % better regarding mortality, from 4 % to 22 % better regarding FCR and from 2 % worse to 49 % better regarding superior product quality.
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Patella, Sergio Maria; Carrese, Filippo; Maltese, Ila; Gatta, Valerio & Marcucci, Edoardo
(2026).
Missing the forest for the trees: what road freight decarbonisation research overlooks.
Transportation Research Part D: Transport and Environment.
ISSN 1361-9209.
154.
doi:
10.1016/j.trd.2025.105207.
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Decarbonising road freight transport is a critical priority for achieving global climate targets. This paper investigates how academic research conceptualises and addresses decarbonisation pathways in road freight transport. It applies a two-step literature review, combining aggregate and disaggregate search strategies, distinguishing between road freight transport and city logistics. Using the Avoid–Shift–Improve framework as an analytical benchmark, findings show that most studies focus on isolated technological solutions, while demand-reduction and modal-shift strategies receive comparatively less attention. Crucially, only a marginal subset of the literature explicitly adopts a structured, integrated, and multidimensional perspective; the majority conducts fragmented and narrowly scoped analyses, limiting the capacity to inform systemic and potentially effective policy strategies. This paper identifies this critical research gap and advocates for a paradigm shift in academic inquiry. Future studies should employ conceptual frameworks that capture the interdependencies among policy measures, thereby supporting the design and implementation of successful freight transport decarbonisation pathways.
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Matos, Saulo; Melo, Rafael A.; Januario, Tiago; Urrutia, Sebastián & Werra, Dominique de
(2026).
On invariants for 1-factorizations of K2n: Description and computation.
Communications in Combinatorics and Optimization (CCO).
ISSN 2538-2128.
11,
s. 1–27.
doi:
10.22049/cco.2025.30792.2619.
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A 1-factorization is a partition of the edge set of a graph into perfect matchings. The concept of 1-factorization is of great interest due to its applications in modeling sports tournaments. An invariant of a 1-factorization is a property that depends only on its structure such that isomorphic 1-factorizations are guaranteed to have equal invariant values. As such, non-isomorphic 1-factorizations may or may not have different invariant values. An invariant is complete when any two non-isomorphic 1-factorizations have distinct invariant values. We review seven invariants available in the literature to distinguish non-isomorphic 1-factorizations of K2n (complete graphs with an even number of vertices). Additionally, considering that the invariants available in the literature are not complete, we propose two new ones, denoted lantern profiles and even-size bichromatic chains. We analyze the invariants concerning their sizes and calculation time complexity. Furthermore, we conduct computational experiments to assess their ability to distinguish non-isomorphic 1-factorizations. To accomplish that we use the sets of non-isomorphic 1-factorizations of K10 and K12. We also consider the sets of non-isomorphic perfect 1-factorizations of K12, K14, and K16, as well as randomly generated 1-factorizations of K16 and K20. Moreover, the experiments evaluate how the combination of invariants can increase the distinguishing ability.
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Esperanca, Marco Delgado; Ferreira, Joao C. & Martins, Ana Lucia
(2026).
The imperative of digital competence in healthcare professionals: comparison between the North and South of Europe.
BMC Health Services Research.
26(1).
doi:
10.1186/s12913-026-14000-8.
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Background
Digital transformation in healthcare increasingly depends on the digitalisation of day-to-day clinical work, in particular on professionals’ ability to use electronic health records, telemedicine, and data-driven tools. However, gaps in digital competence and uneven access to training persist across Europe. This study examines how healthcare professionals understand and apply digital tools in clinical practice, and compares perceived barriers between Northern and Southern Europe.
Methods
We conducted a cross-sectional qualitative survey using a digital self-assessment questionnaire administered to 2,048 healthcare professionals from six European countries. The study was theoretically grounded in the European Digital Competence Framework for Citizens 2.0 (DigComp 2.0) and the eHealth Literacy Framework, and employed thematic analysis to interpret open-ended responses.
Results
Four key thematic areas emerged: remote patient care and monitoring; digital education and health management; transformation of clinical workflows; and data-driven diagnostics. Professionals in Northern Europe—especially Finland—described integrated digital systems, structured training, and institutional support. Participants in Southern Europe more often reported fragmented infrastructure, limited training opportunities, and organizational resistance. The most frequently cited barriers to digital adoption were lack of training (23%), time constraints (27%), limited resources (24%), and resistance to change (19%).
Conclusions
Healthcare professionals widely view digital competence as essential for safe and effective care, but uptake is constrained by structural barriers and regional inequalities. Targeted investment in workforce training, protected time for skill development, and foundational digital infrastructure—particularly in Southern Europe and in resource-limited settings—is needed to support equitable digital transformation across European health systems.
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Phuyal, Sudip; Bhandari, Manila; Bista, Rabindra & Ferreira, Joao C.
(2026).
Enabling Cross-Institution Health Data Sharing in Norway: EUDI Wallets, On-Chain Consent, and openEHR↔FHIR Translation.
IEEE Access.
14,
s. 20309–20327.
doi:
10.1109/access.2026.3661642.
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The cross-border use of health data is still limited by fragmented data models, weak guarantees around who is requesting access, and inconsistent mechanisms for obtaining and revoking patient consent. The European Health Data Space (EHDS) and the European Digital Identity (EUDI) framework aim to address these issues by giving citizens a portable digital identity and legally valid, auditable control over secondary use of their clinical data, but concrete, national-scale implementations are still missing. We present and evaluate a digital health wallet architecture for Norway that (i) issues verifiable credentials to both patients and healthcare professionals; (ii) records, enforces, and revokes patient consent on a permissioned distributed ledger; (iii) verifies access rights using zero-knowledge proofs; and (iv) bridges open Electronic Health Record (openEHR) repositories to EHDS-compliant Fast Healthcare Interoperability Resources (FHIR) bundles. The system is built on Hyperledger Besu, uses Groth16 proof circuits with Poseidon hashing for selective disclosure of consent, and encodes General Data Protection Regulation (GDPR) obligations directly into smart contract logic. The architecture was validated in a simulated hospital environment using synthetic ICU-style data. Professional onboarding and role verification completed in approximately 1.2 s (mean, std. 0.2 s). End-to-end consent issuance, verification, and on-ledger recording completed in under 3 s. On-chain verification of zero-knowledge proofs averaged 0.42 s (std. 0.09 s), supporting near real-time authorization without exposing patient identity. The FHIR–openEHR bridge achieved 98.5% mapping success between openEHR compositions and EHDS-mandated FHIR bundles. We further mapped each component to GDPR Articles 5, 7, 25, and 30, demonstrating explicit, revocable, and auditable consent with privacy by design. These results show that an EUDI-compatible digital health wallet can deliver verifiable identity, revocable consent, privacy-preserving auditability, and semantic interoperability fast enough for clinical use. Unlike prior blockchain proposals for consent management, this work demonstrates not only conceptual alignment with EHDS, but an implemented, measured architecture with national deployment potential.
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Kihei, Billy; Bhavsar, Parth & Ferreira, Joao C.
(2026).
Intelligent Transport Systems : 9th EAI International Conference, INTSYS 2025, Marietta, GA, USA, December 10, 2025, Proceedings.
Springer Nature.
ISBN 9783032387042.
doi:
10.1007/978-3-032-38705-9.
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Hjelle, Harald M.
(2026).
Elektrisk godstransport på veg – største revolusjon siden containeren?
Fulltekst i vitenarkiv
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Isah, Aliyu Buhari; Tolcha, Tassew Dufera & Njoya, Eric Tchouamou
(2026).
Aviation Liberalization and Consumer Welfare Dynamics in Africa.
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Strøm, Roar & Hvattum, Lars Magnus
(2026).
Et internasjonalt forskerteam tror de vet hvem som har størst sjanse til å vinne sommerens fotball-VM.
[Radio].
NRK P1 Møre og Romsdal .
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Henriksen, Vera; Tveten, Erlend Grytli; Midtbø, Lisa Kolden & Klakegg, Øystein
(2026).
Nå kan du lære av ekspertene – helt gratis.
[Journal].
Romsdals Budstikke Pluss.
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Welde, Morten & Tveter, Eivind
(2026).
Stadig lavere avkastning fra nye veiprosjekter.
Moderne Mobilitet og Infrastruktur.
ISSN 2704-1514.
4(1),
s. 1–3.
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Hovde, Katharina & Helgheim, Berit
(2026).
Nytt tilbud ved høgskolen.
[Journal].
Romsdals Budstikke Pluss.
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Fra i høst kan man ta internasjonal master i kunstig intelligens og helselogistikk ved Høgskolen i Molde.
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Steenberg, Glenn R.; Grande, Idar; Morken, Maria Norheim & Bråthen, Svein
(2026).
Kvalitetssikring av et tiltak for Sjøforsvaret (Konfidensiell).
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Nilsen, Steinar; Morken, Maria Norheim; Berggren, Theo; Bakke, Jan Aleksander Olsen; Bråthen, Svein & Steenberg, Glenn
(2026).
KS2 Finansieringsgrunnlag Oslopakke 3 2027-2045 : utarbeidet for Samferdselsdepartementet og Finansdepartementet (Unntatt offentlighet).
Marstrand AS.
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Halse, Askill Harkjerr; Wangsness, Paal Brevik; Babri, Sahar; Tveter, Eivind; Bråthen, Svein & Olsen, Silvia Johanne
(2026).
Veivalg for en realistisk og målrettet transportplan - Svar på første utredningsoppgave fra forskningssenteret TRANSPLAN.
Transportøkonomisk institutt .
ISSN 9788248016540.
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I denne rapporten gjør vi rede for og gir anbefalinger om viktige veivalg som bør tas tidlig i arbeidet med neste Nasjonal transportplan (NTP) for å oppnå en realistisk og målrettet plan, med omstillingen til lavutslippssamfunnet i 2050 som utgangspunkt. Veivalgene er knyttet til fire hovedtemaer. Det første handler om å fremme virkemidler som bidrar til bedre utnyttelse av eksisterende kapasitet i transportsystemet, særlig prising. Det andre handler om å utnytte samspillet mellom de ulike transportformene. Det tredje handler om å prioritere drift, vedlikehold og fornying og synliggjøre den samfunnsøkonomiske gevinsten av dette, med utgangspunkt i et nytt metodisk rammeverk som blir skissert i rapporten. Det er grunn til å tro at økt vedlikeholdsinnsats i mange tilfeller vil være samfunnsøkonomisk lønnsomt. Det fjerde veivalget handler om å legge til rette for informerte beslutninger gjennom en tydeligere struktur og kopling mellom mål og virkemidler, der en synliggjør alternativene til store investeringer som ofte har lav lønnsomhet og bidrar relativt lite til måloppnåelse.
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