Ferreira Joao Carlos

• 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. Full text in Research Archive Show summary

  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.

• Korani, Zahra Mardani; Smet, Thimoty; Challenger, Moharram; Moin, Armin; Ferreira, Joao C. & Silva, Alberto Rodrigues da [Show all 7 contributors for this article] (2026). ML2++ in Practice: A Use-Case-Aware Study of Modeling Effort and Usability for Forecasting-Enabled IoT/CPS. In 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 (Ed.), 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. p. 1145–1154. doi: 10.1145/3837062.3839559. Full text in Research Archive Show summary

  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.

• 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. Full text in Research Archive Show summary

  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.

• 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. Full text in Research Archive Show summary

  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.

• 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. Full text in Research Archive Show summary

  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.

• 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. Full text in Research Archive Show summary

  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.

• 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, p. 1–26. doi: 10.1177/22150218261448184. Full text in Research Archive Show summary

  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.

• 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. Full text in Research Archive Show summary

  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.

• Ferreira, Joao C. (2026). Edge-Assisted Federated Learning with Verifiable Data Provenance for Remote Patient Monitoring over Constrained Networks. In Ukjent (Eds.), 2026 IEEE 18th International Conference on Computational Intelligence and Communication Networks (CICN). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9798331546519. p. 1304–1309. doi: 10.1109/cicn70047.2026.11594160. Full text in Research Archive Show summary

  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.

• Fazendas, Paula; Bairros, Rita; Elvas, Luìs B.; Brochado, Liliana; Ferreira, Joao C. & Gomes, Rita [Show all 13 contributors for this article] (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. Full text in Research Archive Show summary

  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.

• 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. Full text in Research Archive Show summary

  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.

• 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. Full text in Research Archive Show summary

  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.

• 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. Full text in Research Archive Show summary

  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.

• 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, p. 1–1. doi: 10.1109/access.2026.3700449. Full text in Research Archive Show summary

  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).

• 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. Full text in Research Archive
• 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. Full text in Research Archive Show summary

  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.

• 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. Full text in Research Archive
• 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), p. 234–248. doi: 10.1177/18761364251396640. Full text in Research Archive Show summary

  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.

• Ferreira, Joao C.; Correia, Ricardo & Rosário, Luís (2026). Blockchain Integration for Trust and Consent Management in MCP-Enabled FHIR Systems. In Unknown (Eds.), 2025 IEEE International Conference on Distributed Ledger Technologies (ICDLT). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9798331555672. doi: 10.1109/icdlt66400.2025.11466653. Full text in Research Archive Show summary

  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.

• Esperança, Marco; Correia, Ricardo & Ferreira, Joao C. (2026). Blockchain-Based System for Monitoring Vaccine Transportation with Temperature Sensing. In Unknown (Eds.), 2025 IEEE International Conference on Distributed Ledger Technologies (ICDLT). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9798331555672. doi: 10.1109/icdlt66400.2025.11466720. Full text in Research Archive Show summary

  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.

• Pereira, Rui; Boavida, Gonçalo & Ferreira, Joao C. (2026). Blockchain to Ensure Transparency in Lobbying. In Unknown (Eds.), 2025 IEEE International Conference on Distributed Ledger Technologies (ICDLT). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9798331555672. doi: 10.1109/icdlt66400.2025.11467076. Full text in Research Archive Show summary

  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.

• Almeida, Paulo & Ferreira, Joao C. (2026). A Blockchain Architecture for Wood Traceability: A Case Study in the Pulp and Paper Industry. In Unknown (Eds.), 2025 IEEE International Conference on Distributed Ledger Technologies (ICDLT). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9798331555672. doi: 10.1109/icdlt66400.2025.11467098. Full text in Research Archive Show summary

  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.

• 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. In Unknown (Eds.), 2025 IEEE International Conference on Distributed Ledger Technologies (ICDLT). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9798331555672. doi: 10.1109/icdlt66400.2025.11466647. Full text in Research Archive Show summary

  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.

• Curado, Joel; Martins, Ana L & Ferreira, Joao C. (2026). Blockchain Technologies for Traceability and Efficiency in Service-Sector Supply Chains. In Unknown (Eds.), 2025 IEEE International Conference on Distributed Ledger Technologies (ICDLT). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9798331555672. doi: 10.1109/icdlt66400.2025.11467106. Full text in Research Archive Show summary

  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.

• Batista, Bruno L. A.; Correia, Ricardo; Ferreira, Joao C. & Júnior, Joaquim Celestino (2026). AegisChain: Blockchain-Mediated Access Control for Privacy-Preserving DICOM Interoperability. In Unknown (Eds.), 2025 IEEE International Conference on Distributed Ledger Technologies (ICDLT). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9798331555672. doi: 10.1109/icdlt66400.2025.11466719. Full text in Research Archive Show summary

  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.

• 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. Full text in Research Archive Show summary

  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.

• 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. Full text in Research Archive Show summary

  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.

• 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. Full text in Research Archive Show summary

  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.

• 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, p. 35633–35650. doi: 10.1109/access.2026.3670098. Full text in Research Archive Show summary

  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.

• 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. Full text in Research Archive Show summary

  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.

• 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, p. 20309–20327. doi: 10.1109/access.2026.3661642. Full text in Research Archive Show summary

  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.

• Elvas, Luìs B.; Oliveira, Bruno; Mira, Miguel & Ferreira, Joao C. (2025). AI-Enabled Knowledge Creation: A Unified Database Approach in a Multinational Hospital Network. Proceedings of the Annual Hawaii International Conference on System Sciences (HICSS). ISSN 1530-1605. 58, p. 3453–3462. doi: 10.24251/hicss.2025.416. Full text in Research Archive Show summary

  This study outlines an approach for integrating health data from 72 hospitals in 13 countries, focusing on COVID-19 patients with cardiovascular issues. The goal is to combine data within a European project, prioritizing data privacy and utilizing an Extract, Transform, and Load (ETL) architecture for efficient data management. Machine learning, including AI, is applied to predict anomalies in patient data, enhancing the ETL process's capability to support such algorithms. An alert system is established to flag potential outliers for swift medical attention. Challenges such as interoperability and privacy are addressed, and the study evaluates the ETL and AI methods against key performance metrics, confirming their effectiveness. The unified database allows for benchmarking and sharing best practices across hospitals, improving healthcare quality. The paper contributes by detailing ETL development challenges in healthcare and showcasing the benefits of a centralized data repository for healthcare management, particularly through a machine learning algorithm designed to predict abnormal patient values, thereby aiding healthcare professionals in decision-making and improving patient care.

• Raposo, Sara; Mascarenhas, Miguel; Bezerra, Ricardo Correia & Ferreira, Joao C. (2025). Specialised Competencies and Artificial Intelligence in Perioperative Care: Contributions Toward Safer Practice. Healthcare. 13(24). doi: 10.3390/healthcare13243286. Full text in Research Archive Show summary

  This narrative review explores how specialised clinical competencies and artificial intelligence (AI) technologies converge in the context of perioperative care, with a focus on their combined potential to improve patient safety. Considering the growing complexity of surgical care and rising demands on healthcare professionals, the study aims to understand how human expertise and digital tools can complement each other in this high-stakes environment. Methods: A narrative review methodology was adopted to integrate insights from diverse sources, including empirical studies, policy documents, and expert analyses published over the last decade. Findings reveal that AI can support clinical decision-making, streamline workflows, and enable earlier identification of complications across all perioperative phases. These technologies enhance, rather than replace, the roles of nurses, anesthetists, and surgeons. However, their effective use depends on critical factors such as digital literacy, interdisciplinary collaboration, and ethical awareness. Issues related to data privacy, algorithmic bias, and unequal access to technology highlight the need for thoughtful, inclusive implementation. The future of perioperative care will likely depend on hybrid models where human judgment and AI-based tools are integrated in ways that uphold safety, equity, and person-centred values.

• Curado, Joel; Bhandari, Manila; Ferreira, Joao C. & Martins, Ana L. (2025). Blockchain for Maritime Supply Chain: Efficiency and Security Advancements. IEEE Access. 13, p. 201527–201544. doi: 10.1109/access.2025.3636470. Full text in Research Archive Show summary

  The maritime supply chain plays a vital role in global trade, but it continues to face major challenges, including transparency issues, fraud, and data privacy concerns. Blockchain technology has emerged as a promising solution to make the supply chain more secure, efficient, and trustworthy across the system. However, it still encounters limitations, especially regarding privacy and the handling of large volumes of data. To address these issues, Zero-Knowledge Proofs (ZKPs) offer a viable solution, enabling the validation of documents and transactions without revealing sensitive information. This helps maintain confidentiality while meeting regulatory requirements, such as those set by the eFTI regulation. This paper investigates blockchain adoption in maritime supply chains with a focus on ZKP integration for secure document verification, fraud mitigation, and regulatory compliance. It evaluates computational overhead, scalability, and adoption barriers, and proposes a framework supported by simulation-based validation using Ethereum and ZoKrates to assess feasibility and performance. By combining ZKPs with blockchain, this approach enhances a secure, transparent, and efficient trade ecosystem, optimising resources and reducing risks. Future research directions are outlined to advance sustainable maritime logistics.

• Silveirinha, Joel Curado; Bhandari, Manila; Ferreira, Joao C. & Martins, Ana Lúcia (2025). Enhancing maritime supply chain security and efficiency : a review of Zero-Knowledge Proofs in blockchain applications. Maritime Policy & Management. ISSN 0308-8839. p. 23–23. doi: 10.1080/03088839.2025.2580502. Full text in Research Archive Show summary

  Despite the maritime supply chain being the backbone of global trade, it faces persistent challenges in transparency, fraud prevention, shipment tracking and data privacy. Blockchain technology has emerged as a transformative solution, enhancing trust and traceability within supply chain networks. However, its limitations in data privacy and scalability necessitate advanced privacy-preserving mechanisms. Zero-Knowledge Proofs (ZKP) offers a cryptographic approach to validate data without exposing sensitive information, addressing blockchain’s privacy constraints. This paper reviews the state of the art on current applications of blockchain in maritime supply chain management and explores the integration of ZKP for secure trade document verification, fraud detection, privacy-preserving traceability and regulatory compliance. Additionally, it examines computational overhead, scalability and adoption barriers while proposing future research directions. Implementing ZKP within blockchain-based port operations enables robust governance models, ensuring data verification without revealing confidential details. This approach fosters a secure and privacy-compliant trade environment, enhancing trust and collaboration among stakeholders. By optimising resource allocation and mitigating risks, integrating ZKP can significantly improve maritime supply chain efficiency. Integrating Zero-Knowledge Proofs with blockchain, maritime logistics can achieve a balance between transparency, security and operational efficiency, addressing existing challenges in data privacy and regulatory compliance, improving the sustainability of port operations.

• Baral, Sulav; Bista, Rabindra; Sigdel, Sanjog & Ferreira, Joao C. (2025). A hybrid dense convolutional network and fuzzy inference system for pneumonia diagnosis with dynamic symptom tracking. PLOS ONE. 20(10). doi: 10.1371/journal.pone.0334899. Full text in Research Archive Show summary

  Background Pneumonia is a major cause of mortality among children under five and adults over 65, especially in low-resource settings where access to skilled radiologists is limited. Accurate and early diagnosis is essential, but is often hindered by subjective interpretation and variability in its symptoms. Objectives This study aims to develop a hybrid Artificial Intelligence (AI) based pneumonia diagnosis system that integrates Deep Learning (DL) confidence scores, DenseNet201 with Capsule Network (CapsNet), Mamdani-style fuzzy inference, and a dynamic symptom adjustment mechanism to enhance diagnostic accuracy, transparency, and clinical usability. Methods The system was evaluated using 17,229 labelled chest X-ray images across multiple cross-validation techniques: Stratified, k-fold, Bootstrap, and Monte Carlo methods, each with five dataset iterations or folds. DenseNet was used to extract spatial features, while CapsNet preserved spatial orientation and hierarchical relationships. A DL based confidence score was generated and used as a fuzzy membership input to support classification in borderline cases, where severity scores were nearly tied, and the confidence score guided the final decision. A dynamic adjustment algorithm further refined symptom severity by incorporating recent trends in patient data. Results The DenseNet201 + CapsNet architecture achieved the highest performance in the 5th fold of stratified cross-validation, with a test accuracy of 99.01%. The model also demonstrated strong generalization, with a weighted precision, recall and F1-score of 0.9878, 0.9874, and 0.9876, respectively, across all classes. The paired t-test confirmed that the CapsNet-based approach outperformed traditional fully connected layers, and the fuzzy logic system effectively handled ambiguous cases using DL confidence. The dynamic membership mechanism showed strong adaptability for real-time symptom tracking. Conclusion This hybrid model offers a robust, interpretable, and clinically relevant decision-support tool for pneumonia diagnosis. It bridges high-performance AI with real-world medical decision-making, especially in settings with limited radiological expertise.

• Tomaz, Carlos; Marques, Inês; Galheto, Lina; Curado, Joel & Ferreira, Joao C. (2025). Application of blockchain technology in the context of a Security Operations Centre (SOC) log management. In Sakalauskas, Virgilijus; Bajaj, Anu; Abraham, Ajith; Madhavi, K. Reddy & Mishra, Pooja Manghirmalani (Ed.), Bio-Inspired Computing : Proceedings of the 14th International Conference on Innovations in Bio-Inspired Computing and Applications (IBICA 2023) held during December 14-15, 2023, Volume 4: Machine Learning Applications. Springer. ISSN 9783031789465. p. 192–196. doi: 10.1007/978-3-031-78946-5_18. Full text in Research Archive Show summary

  In the era of digital transformation and the ever-evolving threat landscape, Security Operations Centres (SOCs) play a pivotal role in safeguarding organizations against cyberattacks and ensuring data integrity. The management and security of logs generated by various systems and devices are critical aspects of SOC operations. Traditional log management systems, however, face challenges related to data tampering, trust, and transparency. This work aims to evaluate the application of Blockchain technology in one of the operational processes (Security Operations) of companies that provide services in information security. We intend to understand the advantages and challenges of adopting this technology and how it can be a solution to take into account in order to optimize the services provided by these companies, or even give rise to new services.

• Phuyal, Sudip; Elvas, Luìs B.; Ferreira, Joao C. & Bista, Rabindra (2025). Blockchain technology in healthcare : unifying patient medical records : a survey. In Sakalauskas, Virgilijus; Bajaj, Anu; Abraham, Ajith; Madhavi, K. Reddy & Mishra, Pooja Manghirmalani (Ed.), Bio-Inspired Computing : Proceedings of the 14th International Conference on Innovations in Bio-Inspired Computing and Applications (IBICA 2023) held during December 14-15, 2023, Volume 4: Machine Learning Applications. Springer. ISSN 9783031789465. p. 319–332. doi: 10.1007/978-3-031-78946-5_31. Full text in Research Archive Show summary

  In the era of digital transformation, healthcare faces a critical challenge in managing and centralizing patient medical records efficiently and securely. Traditional data management systems, including paper-based records and fragmented electronic health records (EHR) systems, have demonstrated limitations in terms of data security, interoperability, and patient privacy. Blockchain technology has become known as a possible solution to all of these problems, one that has an opportunity to completely transform the administration of healthcare data management. This paper presents a comprehensive exploration of the application of blockchain technology in healthcare, specifically focusing on centralizing patient medical records. Through a literature review and analysis of real-world initiatives, we examine the potential of blockchain to address the shortcomings of existing systems. Blockchain’s key characteristics, such as decentralization, data immutability, and cryptographic security, offer a unique framework for enhancing data security, improving interoperability among healthcare providers, and empowering patients to take control of their health information. The literature review highlights previous research and case studies that illustrate how blockchain has been successfully implemented in healthcare settings. We discuss notable initiatives like MedRec and Medicalchain, showcasing their impact on data security, patient care coordination, and data sharing within the healthcare ecosystem. Consequently, this article offers insightful information about how blockchain technology could revolutionize the handling of healthcare data. It underscores the importance of blockchain as a solution to the challenges associated with centralizing patient medical records, ultimately contributing to improved healthcare outcomes, enhanced patient experiences, and the future evolution of healthcare delivery. This research serves as a resource for healthcare professionals, researchers, and policymakers seeking innovative solutions to enhance data management and patient-centric care in the everevolving healthcare landscape. Blockchain technology adoption in the healthcare industry has the ability to completely transform security of data and accessibility, resulting in a more effective and patient-centered healthcare system.

• Phuyal, Sudip; Elvas, Luìs B.; Ferreira, Joao C. & Bista, Rabindra (2025). Wearable devices for long-term care : survey and opportunities. In Sakalauskas, Virgilijus; Bajaj, Anu; Abraham, Ajith; Madhavi, K. Reddy & Mishra, Pooja Manghirmalani (Ed.), Bio-Inspired Computing : Proceedings of the 14th International Conference on Innovations in Bio-Inspired Computing and Applications (IBICA 2023) held during December 14-15, 2023, Volume 4: Machine Learning Applications. Springer. ISSN 9783031789465. p. 119–128. doi: 10.1007/978-3-031-78946-5_11. Full text in Research Archive Show summary

  The incorporation of wearable devices within long-term care represents a groundbreaking advancement in healthcare. These devices, leveraging sensor and intelligent technologies, possess the potential to revolutionize the supervision and handling of chronic illnesses, enriching the lives of individuals reliant on extended care. This paper conducts an exhaustive examination of wearable devices in the long-term care context, spotlighting their wide-ranging applications. It encompasses devices tailored for monitoring vital signs, mobility, medication adherence, cognitive health, and other critical facets of long-term care. Through an in-depth exploration of their capabilities and limitations, it illuminates their capacity to ease the strain on healthcare systems and enhance patient well-being. Moreover, this paper investigates the hurdles related to wearable device adoption in long-term care, addressing concerns like data security, user acceptance, and interoperability. By tackling these challenges, it endeavors to chart a path for the seamless integration of wearable technologies. Drawing insights from current research and industry trends, it identifies emerging prospects such as personalized care plans, remote monitoring, and data-driven healthcare interventions, fostering collaboration among healthcare stakeholders. By harnessing the full potential of wearable technologies, the quality of life of individuals in long-term care can be improved and also, it can contribute to achieve more efficient and sustainable healthcare systems.

• Elvas, Luìs B.; Santos, Rafaela & Ferreira, Joao C. (2025). Fine-tuning of language models for automated structuring of medical exam reports to improve patient screening and analysis. Scientific Reports. 15. doi: 10.1038/s41598-025-05695-6. Full text in Research Archive Show summary

  The analysis of medical imaging reports is labour-intensive but crucial for accurate diagnosis and effective patient screening. Often presented as unstructured text, these reports require systematic organisation for efficient interpretation. This study applies Natural Language Processing (NLP) techniques tailored for European Portuguese to automate the analysis of cardiology reports, streamlining patient screening. Using a methodology involving tokenization, part-of-speech tagging and manual annotation, the MediAlbertina PT-PT language model was fine-tuned, achieving 96.13% accuracy in entity recognition. The system enables rapid identification of conditions such as aortic stenosis through an interactive interface, substantially reducing the time and effort required for manual review. It also facilitates patient monitoring and disease quantification, optimising healthcare resource allocation. This research highlights the potential of NLP tools in Portuguese healthcare contexts, demonstrating their applicability to medical report analysis and their broader relevance in improving efficiency and decision-making in diverse clinical environments.

• Elvas, Luìs B.; Almeida, Ana & Ferreira, Joao C. (2025). Natural language processing in medical text processing : a scoping literature review. International Journal of Medical Informatics. ISSN 1386-5056. 204(December). doi: 10.1016/j.ijmedinf.2025.106049. Full text in Research Archive Show summary

  Background: The exponential growth of digitized medical data has created significant challenges for healthcare professionals, as medical documentation transitions from simple text records to complex, multi-dimensional data structures. Natural Language Processing (NLP), particularly Named Entity Recognition (NER), has emerged as a crucial tool for extracting and categorizing critical information from clinical texts. The development of transformer-based models like BERT and the ability to fine-tune pre-trained AI models have revolutionized the field, offering unprecedented opportunities to enhance the efficient and precise interpretation of medical data across diverse languages and healthcare contexts. Objective: This literature review aimed to analyze recent NLP approaches for medical text processing, examining techniques, performance metrics, and advancements across different languages and healthcare contexts. Method: Following the Preferred Reporting Items for Systematic Reviews and Meta Analyses (PRISMA) methodology, a scoping search was conducted in Scopus and PubMed databases, focusing on studies published between 2019–2024. The review included studies on language model fine-tuning and information extraction in healthcare, with a specific search query designed to capture relevant NLP techniques. Results: Of 67 initial records, 31 studies were ultimately included. Bidirectional Encoder Representations from Transformers (BERT)-based approaches, neural networks, and CRF/LSTM techniques dominated, consistently achieving F1-scores above 85 %. The studies covered multiple languages, with 51.5 % in English, 27.3 % in Chinese, and smaller representations in Italian, German, and Spanish. Hybrid approaches and techniques addressing data privacy and limited labeled data were notably prevalent. Conclusions: The review revealed that modern NLP techniques, particularly BERT-based models and hybrid approaches, show significant promise in medical text processing across different languages. While challenges remain in cross-lingual adaptation and data availability, these technologies demonstrate potential to enhance medical data interpretation and analysis.

• Ferreira, Joao C. & Esperança, Marco (2025). Enhancing Sustainable Last-Mile Delivery: The Impact of Electric Vehicles and AI Optimization on Urban Logistics. World Electric Vehicle Journal. 16(5). doi: 10.3390/wevj16050242. Full text in Research Archive Show summary

  The rapid growth of e-commerce has intensified the need for efficient and sustainable last-mile delivery solutions in urban environments. This paper explores the integration of electric vehicles (EVs) and artificial intelligence (AI) into a combined framework to enhance the environmental, operational, and economic performance of urban logistics. Through a comprehensive literature review, we examine current trends, technological developments, and implementation challenges at the intersection of smart mobility, green logistics, and digital transformation. We propose an operational framework that leverages AI for route optimization, fleet coordination, and energy management in EV-based delivery networks. This framework is validated through a real-world case study conducted in Lisbon, Portugal, where a logistics provider implemented a city consolidation center model supported by AI-driven optimization tools. Using key performance indicators—including delivery time, energy consumption, fleet utilization, customer satisfaction, and CO₂ emissions—we measure the pre- and post-AI deployment impacts. The results demonstrate significant improvements across all metrics, including a 15–20% reduction in delivery time, a 10–25% gain in energy efficiency, and up to a 40% decrease in emissions. The findings confirm that the synergy between EVs and AI provides a robust and scalable model for achieving sustainable last-mile logistics, supporting broader urban mobility and climate objectives.

• Matos, Kenny; Ribeiro, Ricardo & Ferreira, Joao C. (2025). Mining population opinion about local police. Multimedia Tools and Applications. ISSN 1380-7501. 84(29), p. 35577–35603. doi: 10.1007/s11042-024-20342-4. Full text in Research Archive Show summary

  Sentiment analysis, or opinion mining, is an important task of natural language processing (NLP) that extracts opinions, attitudes, and emotions from text. With the growth of digital platforms like blogs and social networks, opinion mining has become a key tool for organizations to understand public sentiment. In recent research, machine learning and lexicon-based approaches have been applied to analyze sentiments. Our work specifically focuses on national security, where sentiment analysis offers crucial insights into local opinions, helping authorities gauge public mood. As part of our research, we developed the Public Sensing about Police Platform, a prototype system designed to analyze emotions from social networks. This system generates dashboards for law enforcement and security agencies, providing actionable intelligence for public safety. Our findings show that “Hate” was the most common emotion expressed in relation to police interventions, indicating widespread unpopularity of these actions and a resulting sense of insecurity among the public.

• Esperanca, Marco Delgado; Freitas, Diogo; Paixão, Pedro V.; Marcos, Tomás A.; Martins, Rafael A. & Ferreira, Joao C. (2025). Proactive complaint management in public sector informatics using AI : a semantic pattern recognition framework. Applied Sciences. 15(12). doi: 10.3390/app15126673. Full text in Research Archive Show summary

  The digital transformation of public services has led to a surge in the volume and complexity of informatics-related complaints, often marked by ambiguous language, inconsistent terminology, and fragmented reporting. Conventional keyword-based approaches are inadequate for detecting semantically similar issues expressed in diverse ways. This study proposes an AI-powered framework that employs BERT-based sentence embeddings, semantic clustering, and classification algorithms, structured under the CRISP-DM methodology, to standardize and automate complaint analysis. Leveraging real-world interaction logs from a public sector agency, the system harmonizes heterogeneous complaint narratives, uncovers latent issue patterns, and enables early detection of technical and usability problems. The approach is deployed through a real-time dashboard, transforming complaint handling from a reactive to a proactive process. Experimental results show a 27% reduction in repeated complaint categories and a 32% increase in classification efficiency. The study also addresses ethical concerns, including data governance, bias mitigation, and model transparency. This work advances citizen-centric service delivery by demonstrating the scalable application of AI in public sector informatics.

• Ferreira, Joao C.; Elvas, Luìs B.; Correia, Ricardo & Mascarenhas, Miguel (2025). Empowering Health Professionals with Digital Skills to Improve Patient Care and Daily Workflows. Healthcare. 13(3). doi: 10.3390/healthcare13030329. Full text in Research Archive Show summary

  The increasing digitalisation of healthcare has created a pressing need for health professionals to develop robust digital skills. This paper explores the imperative of equipping health professionals with the necessary digital proficiency to enhance their daily workflows and improve patient care. The expanding use of digital technologies, including electronic health records, telehealth, and artificial intelligence, has transformed the healthcare landscape. However, the adoption of these technologies has been hindered by barriers, such as a lack of interoperability and hesitancy among healthcare providers. To address these challenges, this paper argues that digital skill development must be a core component of healthcare education and professional training. Medical schools and healthcare organisations must prioritise the integration of digital health curricula and continuous learning opportunities to ensure that the next generation of healthcare providers is well-equipped to navigate the digital healthcare ecosystem. Additionally, this paper highlights the importance of fostering a culture of digital innovation and collaboration within healthcare settings. By empowering health professionals to actively participate in the development and testing of new digital health applications, the industry can unlock the full potential of digital technologies to enhance daily workflows and improve patient outcomes.

• Elvas, Luìs B.; Almeida, Ana & Ferreira, Joao C. (2025). The Role of AI in Cardiovascular Event Monitoring and Early Detection: Scoping Literature Review. JMIR Medical Informatics. 13. doi: 10.2196/64349. Full text in Research Archive Show summary

  Background: Artificial intelligence (AI) has shown exponential growth and advancements, revolutionizing various fields, including health care. However, domain adaptation remains a significant challenge, as machine learning (ML) models often need to be applied across different health care settings with varying patient demographics and practices. This issue is critical for ensuring effective and equitable AI deployment. Cardiovascular diseases (CVDs), the leading cause of global mortality with 17.9 million annual deaths, encompass conditions like coronary heart disease and hypertension. The increasing availability of medical data, coupled with AI advancements, offers new opportunities for early detection and intervention in cardiovascular events, leveraging AI’s capacity to analyze complex datasets and uncover critical patterns. Objective: This review aims to examine AI methodologies combined with medical data to advance the intelligent monitoring and detection of CVDs, identifying areas for further research to enhance patient outcomes and support early interventions. Methods: This review follows the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology to ensure a rigorous and transparent literature review process. This structured approach facilitated a comprehensive overview of the current state of research in this field. Results: Through the methodology used, 64 documents were retrieved, of which 40 documents met the inclusion criteria. The reviewed papers demonstrate advancements in AI and ML for CVD detection, classification, prediction, diagnosis, and patient monitoring. Techniques such as ensemble learning, deep neural networks, and feature selection improve prediction accuracy over traditional methods. ML models predict cardiovascular events and risks, with applications in monitoring via wearable technology. The integration of AI in health care supports early detection, personalized treatment, and risk assessment, possibly improving the management of CVDs. Conclusions: The study concludes that AI and ML techniques can improve the accuracy of CVD classification, prediction, diagnosis, and monitoring. The integration of multiple data sources and noninvasive methods supports continuous monitoring and early detection. These advancements help enhance CVD management and patient outcomes, indicating the potential for AI to offer more precise and cost-effective solutions in health care.

• Monteiro, Stephanie; Oliveira, Bruno; Elvas, Luìs B. & Ferreira, Joao C. (2025). Improving healthcare data quality : a case study in a Portuguese hospital. Procedia Computer Science. 256, p. 1030–1038. doi: 10.1016/j.procs.2025.02.209. Full text in Research Archive
• Adhikari, Nitanta; Bista, Rabindra & Ferreira, Joao C. (2025). Leveraging machine learning for enhanced bug triaging in open-source software projects. IEEE Access. 13, p. 136237–136254. doi: 10.1109/ACCESS.2025.3595011. Full text in Research Archive Show summary

  Bug triaging–the process of classifying and assigning software issues to appropriate developers–is a critical yet challenging task in large-scale software development. Manual triaging is time-consuming, inconsistent, and prone to human bias, which often delays issue resolution and misallocates developer resources. This study explores the application of machine learning to automate and improve bug triaging efficiency and accuracy. Using a dataset of over 122,000 issues from the microsoft/vscode GitHub repository, we evaluate several machine learning models including Bidirectional LSTM, CNN-LSTM, Random Forest, and Multinomial Naive Bayes. Our primary contribution is the development of an Augmented Bidirectional LSTM model that integrates enriched textual features and contextual metadata. This model, optimized using Optuna, outperforms traditional baselines, achieving a Micro F1-score of 0.6469 and Hamming Loss of 0.0133 for label prediction, and a Micro F1-score of 0.5974 with Hamming Loss of 0.0062 for assignee recommendation. In addition to demonstrating strong predictive performance, we present a robust end-to-end pipeline for data preprocessing, augmentation, model training, and evaluation using multi-label classification techniques. The study highlights how deep learning architectures, in combination with feature engineering and hyperparameter tuning, can provide scalable and generalizable components to support the automation of bug triaging. These findings contribute to the growing field of intelligent software maintenance by offering data-driven approaches that can support developer workflows and improve issue management efficiency in open-source environments.

• Bista, Rabindra; Sharma, Binay; Khanal, Santosh; Sigdel, Sanjog & Ferreira, Joao C. (2025). Improving evaluation measures using ensemble technique in diabetes dataset. In Sakalauskas, Virgilijus; Bajaj, Anu; Abraham, Ajith; Madhavi, K. Reddy & Mishra, Pooja Manghirmalani (Ed.), Bio-Inspired Computing : Proceedings of the 14th International Conference on Innovations in Bio-Inspired Computing and Applications (IBICA 2023) held during December 14-15, 2023, Volume 4: Machine Learning Applications. Springer. ISSN 9783031789465. p. 98–108. doi: 10.1007/978-3-031-78946-5_9. Full text in Research Archive Show summary

  Early Prediction done on the right class for a certain disease in the medical field is very critical and the effects of misclassification of the disease could be very risky which may lead to the mistreatment of the patient. The important classification performance measurements in medical fields are error rate, recall, specificity and accuracy. This research aims to focus on these four different measurements and improvement on the misjudgments while classifying a person to a disease that will provide him/her from getting the correct treatment. Thus, the accuracy in classifying such medical data should be at the highest with lowest error. Many attempts are made in order to identify this problem with the objective of high precision and better accuracy with different machine learning algorithms. Where these machine learning help to identify the disease, prediction of the disease risk, decision making with treatment, disease measurement. This research aims to investigate the performance of the different machine learning algorithms. Diabetes dataset with a total 2000 rows and 8 columns have been analyzed. After all the framework and model being tested with ensemble classifiers and profound with the highest accuracy from proposed weighted ensemble method i.e. 97.5 with minimum error rate of 0.025. Which is greater than neural network and majority ensemble technique applied by previous researcher.

• Bista, Rabindra; Sigdel, Sanjog & Ferreira, Joao C. (2025). Gait analysis for early detection of cardiovascular diseases using MPU-6050 sensor : an analytical framework with data augmentation algorithm. In Sakalauskas, Virgilijus; Bajaj, Anu; Abraham, Ajith; Madhavi, K. Reddy & Mishra, Pooja Manghirmalani (Ed.), Bio-Inspired Computing : Proceedings of the 14th International Conference on Innovations in Bio-Inspired Computing and Applications (IBICA 2023) held during December 14-15, 2023, Volume 4: Machine Learning Applications. Springer. ISSN 9783031789465. p. 109–118. doi: 10.1007/978-3-031-78946-5_10. Full text in Research Archive Show summary

  Walking pattern analysis known as Gait analysis, is one of the key indicators among various clinical parameters, such as cardiovascular disease, in identifying symptoms of diseases. With sensors, the walking patterns of people can be retrieved to identify the differences between patients and healthy controls. Collection of health data is a crucial subject as it requires approval from different individuals/authorities. This paper presents an analysis of Gait datasets with nine Kinematic gait parameters by using classification models, a new data augmentation algorithm. Classification analysis of the Gait dataset has been performed using Neural Network which has 98.65% accuracy. We develop a new GDAA (Gait Data Augmentation Algorithm) for augmentation of Gait data and its time complexity is O(nf). The result of GDAA falls between the minimum range and maximum range of the original dataset. To evaluate optimum classification results, we performed data analysis of the augmented datasets of varying sizes. After a rigorous analysis of augmented data, we found the accuracy of the Neural Network was 97.1%.

• Cordeiro, Manuela & Ferreira, Joao C. (2025). Beyond Traceability: Decentralised Identity and Digital Twins for Verifiable Product Identity in Agri-Food Supply Chains. Applied Sciences. 15(11). doi: 10.3390/app15116062. Full text in Research Archive Show summary

  Agricultural supply chains face growing scrutiny due to rising concerns over food authenticity, safety, and sustainability. These challenges stem from issues such as contamination risks, fraudulent labelling, and the absence of reliable, real-time tracking systems. Existing systems often rely on centralised databases and fragmented data flows, limiting traceability, data integrity, and end-to-end visibility. While blockchain technology offers potential, most research focuses narrowly on traceability, overlooking its role in establishing secure product identity and its integration with emerging tools. This review investigates how Decentralised Identifiers (DIDs), digital twins, and smart contracts—in conjunction with blockchain—can create verifiable digital representations of agricultural products and automate trust mechanisms. Through an analysis of over sixty recent sources and leading standards (e.g., W3C DIDs, Hyperledger Aries), the study identifies key gaps in interoperability, governance, and system maturity. A layered system architecture is proposed, and its application is demonstrated in a cold-chain case scenario. The paper concludes with a roadmap for empirical validation and policy alignment, contributing a practical and scalable framework for researchers, practitioners, and regulators advancing blockchain-enabled traceability systems.

• Nunes, Miguel; Boné, João; Ferreira, Joao C.; Chaves, Pedro & Elvas, Luìs B. (2024). MediAlbertina: An European Portuguese medical language model. Computers in Biology and Medicine. ISSN 0010-4825. 182(November). doi: 10.1016/j.compbiomed.2024.109233. Full text in Research Archive Show summary

  Background: Patient medical information often exists in unstructured text containing abbreviations and acronyms deemed essential to conserve time and space but posing challenges for automated interpretation. Leveraging the efficacy of Transformers in natural language processing, our objective was to use the knowledge acquired by a language model and continue its pre-training to develop an European Portuguese (PT-PT) healthcare-domain language model. Methods: After carrying out a filtering process, Albertina PT-PT 900M was selected as our base language model, and we continued its pre-training using more than 2.6 million electronic medical records from Portugal's largest public hospital. MediAlbertina 900M has been created through domain adaptation on this data using masked language modelling. Results: The comparison with our baseline was made through the usage of both perplexity, which decreased from about 20 to 1.6 values, and the fine-tuning and evaluation of information extraction models such as Named Entity Recognition and Assertion Status. MediAlbertina PT-PT outperformed Albertina PT-PT in both tasks by 4–6% on recall and f1-score. Conclusions: This study contributes with the first publicly available medical language model trained with PT-PT data. It underscores the efficacy of domain adaptation and offers a contribution to the scientific community in overcoming obstacles of non-English languages. With MediAlbertina, further steps can be taken to assist physicians, in creating decision support systems or building medical timelines in order to perform profiling, by fine-tuning MediAlbertina for PT- PT medical tasks.

• Mata, Pedro Neves; Martins, José Moleiro & Ferreira, Joao C. (2024). New Software Product Development: Bibliometric Analysis. Journal of the Knowledge Economy. ISSN 1868-7865. 16(2025)(1), p. 4161–4184. doi: 10.1007/s13132-024-02095-5. Full text in Research Archive Show summary

  The aim of this article is to conduct a comprehensive bibliometric analysis of the existing literature in the domain of new software product development. The research methodology is based on bibliometrics, cluster analysis and meta-analysis of key indicators of knowledge dissemination and publications about the new software product in the Scopus scientometric database for 2003–2022.Through bibliometric analysis, this study provides valuable information on the trends, patterns and impact of research and publications in the field of new software product development. Based on the literature reviewed, known research areas, influential authors and new directions are identified, thus providing a comprehensive overview of the current state of knowledge in the field of new software product development. The number of publications dedicated to new software products has steadily increased over the past two decades, indicating a growing interest in this field among researchers and scholars. The United States, China, and Germany emerged as the leading countries in terms of the number of publications, highlighting their strong presence in software development and technological advancements. Meta-analysis demonstrates the significant impact of Engineering, Mathematics, and Social Sciences on the publication count of the new software product. Overall, the findings highlight the widespread interest and interdisciplinary nature of software product development, with particular emphasis on the role of Computer Science and Engineering in driving advancements in this domain. The conducted bibliometric analysis can serve as a basis for further research and help researchers and practitioners to identify knowledge gaps and directions for future research.

• Elvas, Luìs B.; Nunes, Miguel; Ferreira, Joao C. & Helgheim, Berit (2024). Hospital remote care assistance AI to reduce workload. International Journal of Computer Information Systems and Industrial Management Applications. 16(2), p. 197–209. Full text in Research Archive Show summary

  In the ever-evolving healthcare landscape, the rapid advancement of Artificial Intelligence (AI) technology presents significant opportunities for enhancing patient care and streamlining operational workflows. This study adopts the action research methodology to explore the transformative impact of AI, specifically leveraging data fusion techniques, on remote chronic patient care. Our research yields promising results, achieving 94% accuracy in predicting heart rate abnormalities using Neural Networks and 87.6% for Facial Expression classification through Convolutional Neural Networks. A critical observation from the state-of-the-art indicates that existing studies predominantly focus on remote patient monitoring, leaving a substantial gap in addressing the needs of caregivers and medical staff. Our findings demonstrate that AI, integrated into remote home care monitoring systems, has the potential to alleviate caregiver workloads by enabling continuous patient monitoring and timely alerts. The automation of data extraction and analysis enhances healthcare professionals’ decision-making capabilities, ultimately improving patient outcomes while alleviating the burdens of manual data entry.

• Santos, Nuno; Curado, Joel & Ferreira, Joao C. (2024). Multiparty trust levels in evidence management : ensuring tamper-proof chain of custody in blockchain. Journal of Information Assurance and Security. ISSN 1554-1010. 19(3), p. 80–103. doi: 10.2478/ias-2024-0007. Full text in Research Archive Show summary

  International Criminal Courts (ICCs) are guarantors that justice can be achieved for the most egregious crimes against humanity and that surviving victims of those crimes can live with the assurance that the perpetrators will be held accountable for their actions. Those crimes are complex, sensitive and could be used as a weapon for an intervenient’ s own purpose and interests. As such, the credibility of these institutions is often attacked, and it is of critical importance that the process in which they pursue their mandate is rigorous and effective. Evidence sits at the core of the judicial process, and the participants rely on it to be authentic, integer and untampered with to ensure the fairness of the proceedings. Without enforcing powers, these ICCs could rely extensively on evidence provided by other parties to build a strong case from inception to the appeals stage. With the increased digitization of evidence, cyberthreats, size and complexity of evidence, traditional methods of managing the chain of custody are becoming vulnerable to the successful act of challenging the admissibility of evidence. Blockchain could be the answer for strengthening the chain of custody in evidence management, as this technology brings essential characteristics such as timestamping, authentication, immutability and trust among independent parties. This research conceives and designs a blockchainbased framework that maintains a tamperproof chain of custody and multilevel trust in evidence management. It ensures the authenticity and indisputability of evidence in judicial proceedings.

• Phuyal, Sudip; Elvas, Luìs B.; Ferreira, Joao C. & Bista, Rabindra (2024). Harnessing Wearable Devices for Enhanced Long-Term Care: Opportunities, Challenges, and Future Directions. International Journal of Computer Information Systems and Industrial Management Applications. 16(3), p. 502–514. Full text in Research Archive Show summary

  The incorporation of wearable devices within long-term care represents a groundbreaking advancement in healthcare. These devices, leveraging sensor and intelligent technologies, possess the potential to revolutionize the supervision and handling of chronic illnesses, enriching the lives of individuals reliant on extended care. One of the primary considerations revolves around the question of whether recent technological advancements have made it feasible to deliver healthcare remotely to the elderly population. This paper conducts an exhaustive examination of wearable devices in the long-term care context, spotlighting their wide-ranging applications. It encompasses devices tailored for monitoring vital signs, mobility, medication adherence, cognitive health, and other critical facets of long-term care. Through an in-depth exploration of their capabilities and limitations, it illuminates their capacity to ease the strain on healthcare systems and enhance patient well-being. Moreover, this paper investigates the hurdles related to wearable device adoption in long-term care, addressing concerns like data security, user acceptance, and interoperability. By tackling these challenges, it endeavors to chart a path for the seamless integration of wearable technologies. Drawing insights from current research and industry trends, it identifies emerging prospects such as personalized care plans, remote monitoring, and data-driven healthcare interventions, fostering collaboration among healthcare stakeholders. By harnessing the full potential of wearable technologies, the quality of life of individuals in long-term care can be improved and also, it can contribute to achieve more efficient and sustainable healthcare systems.

• Cale, Daniel; Franco, Adriana; Ferreira, Joao C. & Rocha, Joao (2024). Gamification System for Eco-Driving: Enhancing Driver Motivation and Fuel Savings through Game Mechanics. International Journal of Computer Information Systems and Industrial Management Applications. 16(3), p. 485–501. Full text in Research Archive Show summary

  Implementing a gamification system aims to motivate drivers to adopt eco-friendly driving behaviors and subsequently achieve fuel savings. The growing concern for environmental sustainability and the imperative to reduce carbon emissions from vehicular activities have led to increased interest in eco-driving practices. Gamification, which leverages elements from game design in non-game contexts, offers a promising avenue for fostering behavior change. This research presents a comprehensive framework for developing a gamification system that integrates game mechanics to encourage drivers to engage in fuel-efficient driving practices. This study employs a mixed-method approach that combines data collection from vehicle sensors and user feedback through user testing. The system’s effectiveness regarding fuel savings and driving behavior modifications were evaluated by analyzing the data on driving habits and fuel consumption. The feedback from the participants provides insights into user engagement, motivational factors, and perceptions of the gamified eco-driving experience. Integrating real-time feedback and competitive elements resonates positively with users, stimulating sustained interest and participation. This study demonstrates the potential of gamification as an impactful tool for fostering eco-friendly behaviors and contributing to the broader goals of sustainable transportation and environmental preservation. This investigation contributes to gamification, eco-driving, and sustainable transportation by offering a tangible solution for encouraging positive behavior changes among drivers. The insights gained from this study provide a foundation for future endeavors to design effective gamification systems that address environmental concerns and promote sustainable practices in various domains.

• Ferreira, Joao C.; Elvas, Luìs B.; Correia, Ricardo & Mascarenhas, Miguel (2024). Enhancing EHR Interoperability and Security through Distributed Ledger Technology: A Review. Healthcare. 12(19). doi: 10.3390/healthcare12191967. Full text in Research Archive Show summary

  The management and exchange of electronic health records (EHRs) remain critical challenges in healthcare, with fragmented systems, varied standards, and security concerns hindering seamless interoperability. These challenges compromise patient care and operational efficiency. This paper proposes a novel solution to address these issues by leveraging distributed ledger technology (DLT), including blockchain, to enhance data security, integrity, and transparency in healthcare systems. The decentralized and immutable nature of DLT enables more efficient and secure information exchange across platforms, improving decision-making and coordination of care. This paper outlines a strategic implementation approach, detailing timelines, resource requirements, and stakeholder involvement while addressing crucial privacy and security concerns like encryption and access control. In addition, it explores standards and protocols necessary for achieving interoperability, offering case studies that demonstrate the framework’s effectiveness. This work contributes by introducing a DLT-based solution to the persistent issue of EHR interoperability, providing a novel pathway to secure and efficient health data exchanges. It also identifies the standards and protocols essential for integrating DLT with existing health information systems, thereby facilitating a smoother transition toward enhanced interoperability.

• Elvas, Luìs B.; Nunes, Miguel B.; Helgheim, Berit & Ferreira, Joao C. (2024). Prediction of emergency department operations with artificial intelligence : a case study. In ., IEEE (Eds.), 2024 IEEE 22nd Mediterranean Electrotechnical Conference (MELECON). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9798350387025. p. 473–478. doi: 10.1109/MELECON56669.2024.10608783. Full text in Research Archive Show summary

  The emergency department (ED) of a hospital is a critical component of the healthcare system, serving as the first point of contact for patients in need of immediate medical attention. However, due to the unpredictable volume and nature of demand, managing resources in an ED can be quite challenging. The increasing demand for emergency services has resulted in overcrowding and long wait times, which can negatively impact patient outcomes and satisfaction. In this paper, we propose a solution to these problems by using artificial intelligence (AI) to identify inefficiencies in ED operations and to forecast the patient flow one week in advance. The proposed AI system resorts to machine learning algorithms to analyze the different process phases a patient has to go through while in the ED. By analyzing real-time data, the system can identify bottlenecks and recommend strategies for improving resource allocation, such as adjusting staffing levels or reassigning patients to different areas in the ED. Additionally, the system can also forecast demand for ED services, allowing hospital administrators to proactively allocate resources and ensure that patients receive timely and effective care. The proposed AI-based approach has the potential to significantly improve ED operations and patient outcomes by reducing wait times, improve resource allocation, and improve overall quality of the health service provided. By leveraging the power of AI, hospitals can provide more effective and efficient care, ultimately improving patient satisfaction and outcomes.

• Nunes, Miguel; Boné, João; Ferreira, Joao C. & Elvas, Luìs B. (2024). Health care language models and their fine-tuning for information extraction : scoping review. JMIR Medical Informatics. 12. doi: 10.2196/60164. Full text in Research Archive Show summary

  Background: In response to the intricate language, specialized terminology outside everyday life, and the frequent presence of abbreviations and acronyms inherent in health care text data, domain adaptation techniques have emerged as crucial to transformer-based models. This refinement in the knowledge of the language models (LMs) allows for a better understanding of the medical textual data, which results in an improvement in medical downstream tasks, such as information extraction (IE). We have identified a gap in the literature regarding health care LMs. Therefore, this study presents a scoping literature review investigating domain adaptation methods for transformers in health care, differentiating between English and non-English languages, focusing on Portuguese. Most specifically, we investigated the development of health care LMs, with the aim of comparing Portuguese with other more developed languages to guide the path of a non–English-language with fewer resources. Objective: This study aimed to research health care IE models, regardless of language, to understand the efficacy of transformers and what are the medical entities most commonly extracted. Methods: This scoping review was conducted using the PRISMA-ScR (Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews) methodology on Scopus and Web of Science Core Collection databases. Only studies that mentioned the creation of health care LMs or health care IE models were included, while large language models (LLMs) were excluded. The latest were not included since we wanted to research LMs and not LLMs, which are architecturally different and have distinct purposes. Results: Our search query retrieved 137 studies, 60 of which met the inclusion criteria, and none of them were systematic literature reviews. English and Chinese are the languages with the most health care LMs developed. These languages already have disease-specific LMs, while others only have general–health care LMs. European Portuguese does not have any public health care LM and should take examples from other languages to develop, first, general-health care LMs and then, in an advanced phase, disease-specific LMs. Regarding IE models, transformers were the most commonly used method, and named entity recognition was the most popular topic, with only a few studies mentioning Assertion Status or addressing medical lexical problems. The most extracted entities were diagnosis, posology, and symptoms. Conclusions: The findings indicate that domain adaptation is beneficial, achieving better results in downstream tasks. Our analysis allowed us to understand that the use of transformers is more developed for the English and Chinese languages. European Portuguese lacks relevant studies and should draw examples from other non-English languages to develop these models and drive progress in AI. Health care professionals could benefit from highlighting medically relevant information and optimizing the reading of the textual data, or this information could be used to create patient medical timelines, allowing for profiling.

• Elvas, Luìs B.; Gomes, Sara; Ferreira, Joao C.; Rosário, Luís Brás & Brandão, Tomás (2024). Deep learning for automatic calcium detection in echocardiography. BioData Mining. 17(1). doi: 10.1186/s13040-024-00381-1. Full text in Research Archive Show summary

  Cardiovascular diseases are the main cause of death in the world and cardiovascular imaging techniques are the mainstay of noninvasive diagnosis. Aortic stenosis is a lethal cardiac disease preceded by aortic valve calcification for several years. Data-driven tools developed with Deep Learning (DL) algorithms can process and categorize medical images data, providing fast diagnoses with considered reliability, to improve healthcare effectiveness. A systematic review of DL applications on medical images for pathologic calcium detection concluded that there are established techniques in this field, using primarily CT scans, at the expense of radiation exposure. Echocardiography is an unexplored alternative to detect calcium, but still needs technological developments. In this article, a fully automated method based on Convolutional Neural Networks (CNNs) was developed to detect Aortic Calcification in Echocardiography images, consisting of two essential processes: (1) an object detector to locate aortic valve – achieving 95% of precision and 100% of recall; and (2) a classifier to identify calcium structures in the valve – which achieved 92% of precision and 100% of recall. The outcome of this work is the possibility of automation of the detection with Echocardiography of Aortic Valve Calcification, a lethal and prevalent disease.

• Tokkozhina, Ulpan; Martins, Ana Lucia & Ferreira, Joao C. (2023). Blockchain-based solution for charitable supply chains : network proposal architecture for Portuguese tax consignment program. In Martins, Ana Lucia; Ferreira, Joao C.; Kocian, Alexander; Tokkozhina, Ulpan; Helgheim, Berit & Bråthen, Svein (Ed.), Intelligent Transport Systems : 7th EAI International Conference, INTSYS 2023, Molde, Norway, September 6-7, 2023, Proceedings. Springer. ISSN 9783031493799. p. 92–104. doi: 10.1007/978-3-031-49379-9_5. Full text in Research Archive Show summary

  This paper presents a blockchain-based solution for enhancing transparency, accountability, and efficiency in charitable supply chains, with a focus on the Portuguese Tax Consignment Program, but adaptively to other tax systems. Charitable organizations often face challenges in managing and tracking donations, leading to potential inefficiencies and distrust among stakeholders. Leveraging blockchain technology, this research proposes a network proposal architecture that enables secure and immutable recording of donation transactions, ensures proper allocation of funds, and fosters transparency in the charitable supply chain ecosystem.

• Elvas, Luìs B.; Nunes, Miguel; Francisco, Bruno; Gonçalves, Frederica; Martins, Ana Lucia & Ferreira, Joao C. (2023). Points of interest in smart cities and visitor behavior. In Martins, Ana Lucia; Ferreira, Joao C.; Kocian, Alexander; Tokkozhina, Ulpan; Helgheim, Berit & Bråthen, Svein (Ed.), Intelligent Transport Systems : 7th EAI International Conference, INTSYS 2023, Molde, Norway, September 6-7, 2023, Proceedings. Springer. ISSN 9783031493799. p. 69–91. doi: 10.1007/978-3-031-49379-9_4. Full text in Research Archive Show summary

  Home Intelligent Transport Systems Conference paper Points of Interest in Smart Cities and Visitor Behavior Luís B. Elvas, Miguel Nunes, Bruno Francisco, Frederica Gonçalves, Ana Lucia Martins & Joao Carlos Ferreira Conference paper First Online: 12 December 2023 15 Accesses Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST,volume 540) Abstract Smart cities leverage technology and data to enhance the quality of urban life, including the management of points of interest (POIs) and visitor experiences. This paper explores the relationship between POIs and visitor behavior in smart cities, examining the impact of technology-driven solutions on understanding, analyzing, and optimizing visitor experiences. It highlights the importance of data-driven approaches in identifying and managing POIs, enhancing visitor satisfaction, and driving economic growth. The paper reviews existing literature, discusses key concepts, and presents case studies to illustrate the role of POIs in smart cities and their influence on visitor behavior. Our major contribution is a data driven approach to extract useful information from real data to municipality decisions and understand the problem. It concludes with recommendations for future research and practical implications for city planners, policymakers, and tourism authorities.

• Elvas, Luìs B.; Nunes, Miguel; Ferreira, Joao C.; Francisco, Bruno & Afonso, Jose A. (2023). Georeferenced Analysis of Urban Nightlife and Noise Based on Mobile Phone Data. Applied Sciences. 14(2024)(1). doi: 10.3390/app14010362. Full text in Research Archive Show summary

  Urban environments are characterized by a complex soundscape that varies across different periods and geographical zones. This paper presents a novel approach for analyzing nocturnal urban noise patterns and identifying distinct zones using mobile phone data. Traditional noise-monitoring methods often require specialized equipment and are limited in scope. Our methodology involves gathering audio recordings from city sensors and localization data from mobile phones placed in urban areas over extended periods with a focus on nighttime, when noise profiles shift significantly. By leveraging machine learning techniques, the developed system processes the audio data to extract noise features indicative of different sound sources and intensities. These features are correlated with geographic location data to create comprehensive city noise maps during nighttime hours. Furthermore, this work employs clustering algorithms to identify distinct noise zones within the urban landscape, characterized by their unique noise signatures, reflecting the mix of anthropogenic and environmental noise sources. Our results demonstrate the effectiveness of using mobile phone data for nocturnal noise analysis and zone identification. The derived noise maps and zones identification provide insights into noise pollution patterns and offer valuable information for policymakers, urban planners, and public health officials to make informed decisions about noise mitigation efforts and urban development.

• Tokkozhina, Ulpan; Martins, Ana Lucia & Ferreira, Joao C. (2023). Blockchain-powered fish industry : trust perceptions of final consumers on traceable information availability. In Martins, Ana Lucia; Ferreira, Joao C.; Kocian, Alexander; Tokkozhina, Ulpan; Helgheim, Berit & Bråthen, Svein (Ed.), Intelligent Transport Systems : 7th EAI International Conference, INTSYS 2023, Molde, Norway, September 6-7, 2023, Proceedings. Springer. ISSN 9783031493799. p. 151–162. doi: 10.1007/978-3-031-49379-9_8. Full text in Research Archive Show summary

  Blockchain is being actively discussed for business applications to digitalise supply chains. The still nascent level of blockchain adoption creates a difficulty to see the potential value that it may add to products and final consumers. Through a quantitative method, this study explores the impact of the traceability feature of products on the trust in the seller supermarket chain from the perspective of final consumers. Data from 417 questionnaires applied to buyers of fish was used to analyse such relation. Findings reveal the interest of final consumers towards traceable products and a positive relationship between the traceable information availability and the trust towards product.

• Tokkozhina, Ulpan; Mataloto, Bruno Miguel; Martins, Ana Lucia & Ferreira, Joao C. (2023). Decentralizing online food delivery services : a blockchain and IoT model for smart cities. Mobile Networks and Applications. ISSN 1383-469X. doi: 10.1007/s11036-023-02119-5. Full text in Research Archive Show summary

  With the worldwide pandemic outbreak, the style of restaurant food consumption underwent a major shift towards online delivery services. This poses an urgent need in trust between main stakeholders of the process: requiring restaurants to correspond to the quality declared, providing high quality and safe to consume meals, and obliging delivery entities to deliver food carefully, scrupulously following the delivery conditions. In this research, we explore a novelty approach that combines IoT, Blockchain technology, and city LoRa network to create a new trusted, decentralized approach for food distribution process in the context of a Smart City. This approach allows controlling the food delivery process using sensors data to control live location, temperature, vibrations, and shakings during the transportation process. We also suggest a fresh perspective on a rating system of delivery entities, where reputation points will be provided both from the side of the restaurant and the final consumer. This will create more trust towards the delivery entity since information will be tamper-proof and immutable due to the nature of Blockchain. This novel system proposal allows rethinking the online food delivery process in the context of Smart City, using the city’s LoRa LPWAN radio frequency technology and Blockchain decentralized solution.

• Pereira, José; Antunes, Nuno; Rosa, Joana; Ferreira, Joao C.; Mogo, Sandra & Pereira, Manuel (2023). Intelligent Clinical Decision Support System for Managing COPD Patients. Journal of Personalized Medicine. 13(9), p. 1–34. doi: 10.3390/jpm13091359. Full text in Research Archive Show summary

  Chronic obstructive pulmonary disease (COPD) is the third leading cause of death worldwide. Health remote monitoring systems (HRMSs) play a crucial role in managing COPD patients by identifying anomalies in their biometric signs and alerting healthcare professionals. By analyzing the relationships between biometric signs and environmental factors, it is possible to develop artificial intelligence models that are capable of inferring patients’ future health deterioration risks. In this research work, we review recent works in this area and develop an intelligent clinical decision support system (CIDSS) that is capable of providing early information concerning patient health evolution and risk analysis in order to support the treatment of COPD patients. The present work’s CIDSS is composed of two main modules: the vital signs prediction module and the early warning score calculation module, which generate the patient health information and deterioration risks, respectively. Additionally, the CIDSS generates alerts whenever a biometric sign measurement falls outside the allowed range for a patient or in case a basal value changes significantly. Finally, the system was implemented and assessed in a real case and validated in clinical terms through an evaluation survey answered by healthcare professionals involved in the project. In conclusion, the CIDSS proves to be a useful and valuable tool for medical and healthcare professionals, enabling proactive intervention and facilitating adjustments to the medical treatment of patients. Keywords: chronic obstructive pulmonary disease (COPD), decision support system (DSS), intelligent clinical decision support system (CIDSS), health remote monitoring system (HRMS), triage validation module (TVM)

• Pires, Ana Rita; Ferreira, Joao C. & Klakegg, Øystein (2023). The future in fishfarms : an ocean of technologies to explore. In Abraham, Ajith; Bajaj, Anu; Gandhi, Niketa; Madureira, Ana Maria & Kahraman, Cengiz (Ed.), Innovations in Bio-Inspired Computing and Applications : Proceedings of the 13th International Conference on Innovations in Bio-Inspired Computing and Applications (IBICA 2022) Held During December 15-17, 2022. Springer. ISSN 9783031274985. p. 318–326. doi: 10.1007/978-3-031-27499-2_30. Full text in Research Archive Show summary

  We present the potential application of Machine Learning (ML) to fish farm in a similar approach used in agriculture to control crop growing and predict diseases. The agriculture concept of Precision Agriculture is now applied to fish farm by applying control-engineering principles to fish production; Precision Fish Farming (PFF) aims to improve the farmer's ability to monitor, control, and document biological processes. PFF can help the industry because it takes into consideration the boundary conditions and potentials that are unique to farming operations in the aquatic environment. The proposed solution improves commercial aquaculture and makes it possible to transition to knowledge-based production regime as opposed to experience-based. We apply a data mining approach to identify and evaluate the impact on the growth and mortality of fish in hatcheries. The use of ML techniques, combined with regulation, can increase the productivity and welfare of aquaculture living organisms. Keywords: machine learning, precision fish farming, data analytics, IoT, water quality

• Ferreira, Joao C.; Francisco, Bruno; Elvas, Luìs B.; Nunes, Miguel & Afonso, Jose A. (2023). Predicting People’s Concentration and Movements in a Smart City. Electronics (Basel). 13(2024)(1). doi: 10.3390/electronics13010096. Full text in Research Archive Show summary

  first_pagesettingsOrder Article Reprints Open AccessArticle Predicting People’s Concentration and Movements in a Smart City by Joao C. Ferreira 1,2,3ORCID,Bruno Francisco 1,Luis Elvas 1,2,3,Miguel Nunes 1 andJose A. Afonso 4,*ORCID 1 Instituto Universitário de Lisboa (ISCTE-IUL), ISTAR, 1649-026 Lisboa, Portugal 2 Inov Inesc Inovação—Instituto de Novas Tecnologias, 1000-029 Lisbon, Portugal 3 Department of Logistics, Molde University College, 6410 Molde, Norway 4 CMEMS-UMinho/LABBELS—Associate Laboratory, University of Minho, 4800-058 Guimarães, Portugal * Author to whom correspondence should be addressed. Electronics 2024, 13(1), 96; https://doi.org/10.3390/electronics13010096 Submission received: 25 October 2023 / Revised: 18 December 2023 / Accepted: 21 December 2023 / Published: 25 December 2023 (This article belongs to the Special Issue AI Technologies and Smart City) Downloadkeyboard_arrow_down Browse Figures Versions Notes Abstract With the rapid growth of urbanization and the proliferation of mobile phone usage, smart city initiatives have gained momentum in leveraging data-driven insights to enhance urban planning and resource allocation. This paper proposes a novel approach for predicting people’s concentration and movements within a smart city environment using mobile phone data provided by telecommunication operators. By harnessing the vast amount of anonymized and aggregated mobile phone data, we present a predictive framework that offers valuable insights into urban dynamics. The methodology involves collecting and processing location-based data obtained from telecommunication operators. Using machine learning techniques, including clustering and spatiotemporal analysis, we developed models to identify patterns in people’s movements and concentration across various city regions. Our proposed approach considers factors such as time of day, day of the week, and special events to capture the intricate dynamics of urban activities. The predictive models presented in this paper demonstrate the ability to predict areas of high concentration of people, such as commercial districts during peak hours, as well as the people flow during the time. These insights have significant implications for urban planning, traffic management, and resource allocation. Our approach respects user privacy by working with aggregated and anonymized data, ensuring compliance with privacy regulations and ethical considerations. The proposed models were evaluated using real-world mobile phone data collected from a smart city environment in Lisbon, Portugal. The experimental results demonstrate the accuracy and effectiveness of our approach in predicting people’s movements and concentration. This paper contributes to the growing field of smart city research by providing a data-driven solution for enhancing urban planning and resource allocation strategies. As cities continue to evolve, leveraging mobile phone data from telecommunication operators can lead to more efficient and sustainable urban environments.

• Tokkozhina, Ulpan; Martins, Ana Lucia & Ferreira, Joao C. (2023). Multi-tier supply chain behavior with blockchain technology : evidence from a frozen fish supply chain. Operations Management Research. ISSN 1936-9735. 16(3), p. 1562–1576. doi: 10.1007/s12063-023-00377-w. Full text in Research Archive Show summary

  Relationships in multi-tier supply chains (MTSCs) are complex and require constant information sharing. For MTSCs involved in perishable product distribution, blockchain technology (BCT) is one of the most promising technologies that can ensure products’ traceability and safety. This study examines a Portuguese MTSC for frozen fish products piloting BCT adoption for its supply chain (SC) practices. The goal is therefore to explore the consequences of information availability inherent for BCT adoption pilots in the SC context. This study follows a mixed-method approach, with qualitative and quantitative data collection techniques. Three semi-structured interviews with players from a single frozen fish SC, including supplier, transporter, and retailer were conducted. MAXQDA Analytics Pro 2022 software was used for further coding and data analysis. An online survey among retail chain consumers was conducted resulting in 112 responses. Regression analysis and Pearson correlation test were further performed via IBM SPSS Statistics software. Data collection for both parts took place between December 2021 and July 2022. This study explores four layers in the SC– including supplier, transporter, retailer and final consumer. Findings revealed a current lack of extensive and detailed information from upper tiers regarding products’ characteristics. Despite being hyped as a “trust-enabling” technology, BCT was found not to eliminate the need for trustful relationships between players prior to adoption, as information input requires human intervention. Even in those cases when the traceability option is not of major interest to final consumers, findings revealed a higher probability of purchasing fish products that have traceable information available.

• Tokkozhina, Ulpan; Martins, Ana Lúcia & Ferreira, Joao C. (2022). Use of blockchain technology to manage the supply chains : comparison of perspectives between technology providers and early industry adopters. Journal of Theoretical and Applied Electronic Commerce Research. 17(4), p. 1616–1632. doi: 10.3390/jtaer17040082. Full text in Research Archive Show summary

  Following the interest in blockchain technology (BCT) business solutions and the nascent stage of technology in supply chain (SC) practices, this research compares views from business practitioners who are experienced in real cases of BCT adoption with the views of technology consultants proficient in the complexities of BCT to analyze the benefits and challenges BCT holds for SCs. Based on the comparison of the two sides, the joint views that both adopters and technology consultants share is the ability that BCT holds to speed up processes across SCs through decentralized data access, thus decreasing human errors and reducing paperwork. However, technology consultants perceive the need to increase BCT awareness levels of businesses, to prevent BCT implementation just for reasons such as ‘recordkeeping’ and to reduce misconceptions in areas such as cryptocurrency applications. The findings also revealed that technology consultants insist on the careful evaluation and definition of records to be kept on BCT platforms prior to the adoption process, in order to avoid unnecessary data input. Currently, according to early industry adopters’ cases, most business attempts of BCT adoption use private networks, so technology consultants promote business entities on developing plans towards open-access public networks. Keywords: supply chain management, blockchain, early industry adopters, technology consultants, semi-structured interviews

• Ferreira, Joao C.; Martins, Ana Lucia; Tokkozhina, Ulpan & Helgheim, Berit (2022). Fish control process and traceability for value creation using blockchain technology. In Abraham, Ajith; Madureira, Ana Maria; Kaklauskas, Arturas; Gandhi, Niketa; Bajaj, Anu; Muda, Azah Kamilah; Kriksciuniene, Dalia & Ferreira, Joao C. (Ed.), Innovations in Bio-Inspired Computing and Applications : Proceedings of the 12th International Conference on Innovations in Bio-Inspired Computing and Applications (IBICA 2021) Held During December 16–18, 2021. Springer. ISSN 9783030962982. p. 761–773. doi: 10.1007/978-3-030-96299-9_72. Full text in Research Archive Show summary

  Currently, the globe and, more specifically, corporate processes are evolving towards digitization and the elimination of time-consuming procedures. As origin knowledge and safety assurance of products became more important in establishing end-consumer confidence, traceability solutions are acute. A Blockchain (BC) is one of the most exciting disruptive technologies emerging today. The purpose of this study is to analyze the possibility of using BC to allow end-to-end supply chain (SC) management traceability and to provide a proposition of a BC application for fish tracking and control in SC contexts. The proposition demonstrates that BC provides the use of a single information system by several players across SC, resulting in more secure and efficient data sharing. A future understanding of the significance of BC technology usage as a traceability supplier and a detailed testing of a proposed architecture is identified as a diretion for future study. We, therefore, study current applications in supply chain management, with a big emphasis on the fish control process. Keywords: blockchain, traceability, supply chain, IoT, fish

• Tokkozhina, Ulpan; Martins, Ana Lucia & Ferreira, Joao C. (2022). Uncovering dimensions of the impact of blockchain technology in supply chain management. Operations Management Research. ISSN 1936-9735. 16(1), p. 99–125. doi: 10.1007/s12063-022-00273-9. Full text in Research Archive Show summary

  Supply chains around the globe are faced with difficulties and disruptions due to the worldwide pandemic situation and digital solutions are needed. There is significant research interest in the implementation of blockchain technology (BCT) for supply chain management (SCM). A challenge that remains is analyzing the interactions of BCT in different areas of SCM. This study aims to identify the influential dimensions of the impact of BCT adoption in SCM and to discuss the synergetic and counter-synergetic effects between these dimensions. Advantages, disadvantages, and constraints of adopting BCT in the SCM context are explored through a systematic literature review, which provides the foundation for identifying the dimensions of impact. The interactions between these dimensions are conceptually discussed. This study introduces three dimensions of the impact of implementing BCT in SCM: ‘operations and processes’, ‘supply chain relationships’, and ‘innovation and data access’. These dimensions are interrelated and have overlapping areas within them, which leads to synergetic and counter-synergetic effects. The overlaps and synergies of the three dimensions of impact are illustrated, and the virtuous and vicious cycles of BCT adoption in SCM cases are highlighted. This study assists scholars and practitioners by clarifying the synergetic relationships within the dimensions of the impact of BCT in SCM and by providing considerations to prevent undesirable effects and expand desired ones.

• Figueiredo, Ruben E.; Monteiro, Vitor; Ferreira, Joao C.; Afonso, Joao L. & Afonso, Jose A. (2021). Smart home power management system for electric vehicle battery charger and electrical appliance control. International Transactions on Electrical Energy Systems. 31(4). doi: 10.1002/2050-7038.12812. Full text in Research Archive Show summary

  This article presents a power management system (PMS) designed for smart homes aiming to deal with the new challenges imposed by the proliferation of plug-in electric vehicles (EVs) and their coexistence with other residential electrical appliances. The PMS is based on a hybrid wireless network architecture composed by a local hub/gateway and several Bluetooth Low Energy (BLE) and Wi-Fi sensor/actuator devices. These wireless devices are used to transfer information inside the smart home using the Message Queuing Telemetry Transport (MQTT) protocol. Based on the proposed solution, the current consumption of the EV battery charger and other residential electrical appliances are dynamically monitored and controlled by using a configurable algorithm, ensuring that the total current consumption does not cause the tripping of the home circuit breaker. An Android client application allows the user to monitor and configure the system operation in real-time, a developed Wi-Fi smart plug permits to measure the RMS values of current of the connected electrical appliance and change its state of operation remotely, and an EV battery charger may be controlled in terms of operating power according to set-points received from the Android client application. Experimental tests are used to evaluate the quality of service provided by the developed smart home platform in terms of communication delay and reliability. An experimental validation for different conditions of operation of the proposed smart home PMS concerning the power operation of the EV battery charger with the proposed control algorithm is also presented.

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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. Full text in Research Archive
• Martins, Ana Lucia; Ferreira, Joao C.; Kocian, Alexander; Tokkozhina, Ulpan; Helgheim, Berit & Bråthen, Svein (2023). Intelligent Transport Systems : 7th EAI International Conference, INTSYS 2023, Molde, Norway, September 6-7, 2023, Proceedings. Springer. ISBN 9783031493799. 259 p. Full text in Research Archive Show summary

  This book constitutes the refereed proceedings of the 7th International Conference on Intelligent Transport Systems, INTSYS 2023, held in Molde, Norway, during September 6-7, 2023. With the globalization of trade and transportation and the consequent multi-modal solutions used, additional challenges are faced by organizations and countries. Intelligent Transport Systems make transport safer, more efficient, and more sustainable by applying information and communication technologies to all transportation modes. The 14 full papers included in this book were carefully reviewed and selected from 39 submissions and are organized in three thematic sessions on transportation; smart cities and blockchain; networks.

• Abraham, Ajith; Madureira, Ana Maria; Kaklauskas, Arturas; Gandhi, Niketa; Bajaj, Anu & Muda, Azah Kamilah [Show all 8 contributors for this article] (2022). Innovations in Bio-Inspired Computing and Applications : Proceedings of the 12th International Conference on Innovations in Bio-Inspired Computing and Applications (IBICA 2021) Held During December 16–18, 2021. Springer. ISBN 9783030962982. 192 p. Full text in Research Archive

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• Ferreira, Joao C.; Esperança, Marco; Aravazhi, Agaraoli & Helgheim, Berit (2025). LLM4Autism: A Local, Personalized AI Assistant for Autism Care. doi: 10.46254/IN05.20250112. Full text in Research Archive Show summary

  Autism Spectrum Disorder presents unique challenges in daily life for patients, families, and caregivers, often requiring personalized, real-time support. LLM4Autism is a novel edge-deployed Large Language Model designed to enhance autonomy, reduce disability severity, and promote social inclusion through local, privacy-preserving AI assistance. Leveraging federated learning, multimodal data integration, and explainable AI, the system processes behavioral, physiological, and contextual inputs (e.g., EEG, HRV, video, caregiver text) to provide tailored prompts, early warnings, and insights. Importantly, we acknowledge the substantial technical difficulty of fusing these heterogeneous data streams in real time on constrained devices: sampling-rate mismatches, synchronization and alignment across modalities, noise/missingness, and strict latency/memory budgets. Our architecture addresses these constraints via parameter-efficient fine-tuning and quantization for on-device inference, adaptive sampling and late/early fusion strategies, time-code alignment with fail-safe degradation paths, and privacy-by-design federated updates. This paper outlines the system's architecture, scientific foundations via a comprehensive literature review, alignment with project goals, and validation through four diverse use case scenarios. We discuss risks and open issues—including edge performance trade-offs, bias, and governance—alongside future directions. LLM4Autism represents a pragmatic, GDPR-compliant step toward trustworthy, local AI for ASD care developed with stakeholder engagement.

• Ferreira, Joao C.; Elvas, Luìs B.; Correia, Ricardo & Mascarenhas, Miguel (2025). Tokenization of electronic health records and healthcare data: enhancing security and privacy while enabling usability. Health and Technology. ISSN 2190-7188. 15(6), p. 1011–1020. doi: 10.1007/s12553-025-01012-3. Full text in Research Archive Show summary

  Abstract Purpose The purpose of this paper is to examine the role of tokenization in protecting Electronic Health Records (EHRs) and healthcare data, with a focus on ensuring data security and privacy compliance. Tokenization, which replaces sensitive patient data with unique identifiers (tokens), is explored as a means to prevent unauthorized access while maintaining data utility for healthcare operations, research, and analytics. Additionally, the paper aims to assess how tokenization helps healthcare organizations comply with regulations like HIPAA, and how it facilitates secure data sharing and analytics. Methods This state-of-the-art review analyzes the latest advancements in tokenization techniques, particularly their applications in safeguarding patient data during storage, transmission, and analysis. The review includes an exploration of tokenization’s role in secure data exchange, system interoperability, and the integration of natural language processing (NLP) for analyzing de-identified healthcare data. Various methodologies and real-world implementations of tokenization in healthcare are comprehensively assessed. Results The analysis reveals that tokenization effectively enhances data security and privacy, supporting compliance with stringent regulatory frameworks like HIPAA. Furthermore, tokenization enables the secure exchange of healthcare data across systems while maintaining interoperability. The integration of NLP for de-identified data analysis further expands its application in research and analytics without compromising patient confidentiality. Conclusions The review highlights the potential of tokenization to balance data security and usability within healthcare systems. It improves patient care by facilitating secure data sharing and supporting advanced analytics, while simultaneously ensuring operational efficiency and regulatory compliance. Tokenization emerges as a crucial tool in enhancing the protection and utility of healthcare data.

• Ferreira, Joao C. (2025). Digital wallets : unlocking secure health data access and clinical summaries. Full text in Research Archive
• Santos, Nuno; Curado, Joel & Ferreira, Joao C. (2025). Multiparty trust levels in evidence management : ensuring tamper-proof chain of custody in blockchain. In Sakalauskas, Virgilijus; Bajaj, Anu; Abraham, Ajith; Madhavi, K. Reddy & Mishra, Pooja Manghirmalani (Ed.), Bio-Inspired Computing : Proceedings of the 14th International Conference on Innovations in Bio-Inspired Computing and Applications (IBICA 2023) held during December 14-15, 2023, Volume 4: Machine Learning Applications. Springer. ISSN 9783031789465. p. 197–209. doi: 10.1007/978-3-031-78946-5_19. Full text in Research Archive Show summary

  International Criminal Courts are a guarantor that justice can be achieved for the most egregious crimes against humanity and that surviving victims from those crimes can live with the assurance that the perpetrators are held accountable for their actions. Evidence sits at the core of the judicial process, and the participants rely on it to be authentic, integer and untampered with to ensure the fairness of the proceedings. Without enforcing powers, these International Criminal Courts could rely extensively on evidence provided by other parties to build a strong case from inception to appeals stage. With the increased digitization, size and complexity of evidence, traditional methods of managing the chain of custody are becoming vulnerable to successful challenging of admissibility of evidence. Blockchain could be the answer for strengthening the Chain of Custody in evidence management and mitigate these identified threats, as this technology brings essential characteristics such as time-stamping, authentication, immutably and trust among independent parties. This paper conceives and designs a blockchain-based solution that maintains a tamper proof chain of custody and multilevel trust in evidence management applied to the International Criminal Court. The convergence of International Criminal Justice with Blockchain Technology is notably absent within existing academic discourse, and arguably in one of the most unstable and conflictual periods in human history, this paper offers a timely approach to deliver justice in a more effective and credible way.

• Ferreira, Joao C. (2025). Experience-based master's as a way of connecting the industry to the university. Full text in Research Archive Show summary

  Participation at IEEE Industry panel

• Ferreira, Joao C. (2025). Frigjør potensialet til KI og blokkjedeteknologi i næringslivet. Full text in Research Archive
• Ferreira, Joao C. (2025). Bridging the Gaps: Future Directions for Blockchain and IoT Integration in Smart Grids. Energies. 18(4). doi: 10.3390/en18040772. Full text in Research Archive
• Elvas, Luìs B.; Ferreira, Joao C. & Helgheim, Berit (2025). Hospital remote care assistance AI to reduce workload. In Sakalauskas, Virgilijus; Bajaj, Anu; Abraham, Ajith; Madhavi, K. Reddy & Mishra, Pooja Manghirmalani (Ed.), Bio-Inspired Computing : Proceedings of the 14th International Conference on Innovations in Bio-Inspired Computing and Applications (IBICA 2023) held during December 14-15, 2023, Volume 4: Machine Learning Applications. Springer. ISSN 9783031789465. p. 61–72. doi: 10.1007/978-3-031-78946-5_6. Full text in Research Archive Show summary

  This research aims to create a remote system to reduce the Hospital load for patients with chronic diseases at Home or Recover Centers. A framework of shared Artificial intelligence services is proposed to provide a secure environment for remote patient monitoring. A literature review using the PRISMA methodology provides a knowledge systematization. A new data fusion process with data, image, text, and voice allows for improving remote patient data collected. We apply Design Science Research with CRISP-DM approach to handling the data process. We are developing specific modules to fuse and handle image, voice, text and data. Our model is being validated and applied in a real case from a Portuguese hospital using Alzheimer’s patients. In our research work, we propose a framework for secure information sharing among patients’ data and improving health care services. This approach allows the creation of big data systems in a fusion of image, text, voice, and data. The quick development of AI and associated technologies would assist healthcare providers in enhancing patient value and streamlining operational procedures. In this study, we examined how AI technology with data fusion for chronic patients allows professional hospital visits and reduces their workload. Effective management of these opportunities and challenges requires the joint knowledge and tenacity of all healthcare industry players.

• Franco, Adriana; Cale, Daniel & Ferreira, Joao C. (2025). Gamification system for eco-driving : enhancing driver motivation and fuel savings through game mechanics. In Sakalauskas, Virgilijus; Bajaj, Anu; Abraham, Ajith; Madhavi, K. Reddy & Mishra, Pooja Manghirmalani (Ed.), Bio-Inspired Computing : Proceedings of the 14th International Conference on Innovations in Bio-Inspired Computing and Applications (IBICA 2023) held during December 14-15, 2023, Volume 4: Machine Learning Applications. Springer. ISSN 9783031789465. p. 129–137. doi: 10.1007/978-3-031-78946-5_12. Full text in Research Archive Show summary

  Implementing a gamification system aimed at motivating drivers to adopt eco-friendly driving behaviors and subsequently achieve fuel savings. The growing concern for environmental sustainability and the imperative to reduce carbon emissions from vehicular activities have led to an increased interest in eco-driving practices. Gamification, leveraging elements from game design in non-game contexts, offers a promising avenue for fostering behavior change. This research presents a comprehensive framework for developing a gamification system that integrates game mechanics to encourage and incentivize drivers to engage in fuel-efficient driving practices. The study employs a mixed-methods approach, combining data collection from vehicle sensors and user feedback through user testing. The gamification system is meticulously designed, incorporating diverse game mechanics such as challenges, rewards, leaderboards, and progress tracking. By analyzing data on driving habits and fuel consumption, the effectiveness of the system is evaluated in terms of fuel savings and driving behavior modifications. The feedback from participants provides insights into user engagement, motivational factors, and perceptions of the gamified eco-driving experience. The findings indicate that the gamification system significantly influences driver behavior, resulting in reduced fuel consumption and heightened motivation to practice eco-driving techniques. The integration of real-time feedback and competitive elements resonates positively with users, stimulating sustained interest and participation. The study demonstrates the potential of gamification as an impactful tool for fostering eco-friendly behaviors and contributing to the broader goals of sustainable transportation and environmental preservation. Through this research, this investigation contributes to the fields of gamification, eco-driving, and sustainable transportation by offering a tangible solution for encouraging positive behavior changes among drivers. The insights gained from the study provide a foundation for future endeavors in designing effective gamification systems that address enviro

• Stokholm, Andreas Brandtun; Wilkensen, Sindre & Ferreira, Joao C. (2025). Blockchain's potential role in enhancing data-security and privacy in Smart Cities. Høgskolen i Molde - Vitenskapelig høgskole i logistikk. Full text in Research Archive

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