The evolution from the Edge-Cloud Continuum to the Cognitive Computing Continuum (CCC) has introduced new challenges which necessitate advanced frameworks that integrate cognitive capabilities to enhance interoperability, adaptability, and resource efficiency. Considering insights from ongoing research and initiatives on the cognitive cloud, we identify the core concepts essential for transitioning to the CCC, including cognitive orchestration, distributed AI, and sovereign data management. We then introduce ENACT, a novel framework designed to embrace these concepts aiming to provide cognitive, highly adaptive orchestration to support modern hyper-distributed and data-intensive applications. Furthermore, ENACT employs bespoke mechanisms to enable dynamic continuum modelling and visibility as well as to facilitate application-level automation and adaptability. This paper presents the motivation behind the ENACT approach, reviews the state-of-the-art across its fundamental technological concepts and highlights its key innovations. Overall, it contributes to the formalization of the CCC architectural principles and, ultimately, to the realization of CCC.

Zero-touch provisioning (ZTP) is a key enabler for scalable deployment of edge infrastructure across heterogeneous networks. This paper presents a decentralized ZTP framework developed as part of the ENACT project, designed to automate the discovery, configuration, and integration of remote devices without requiring direct access or manual intervention. Built on open standards and widely used technologies—including PXE, DHCP, Cloud-Init, and REST APIs—the system minimizes administrative overhead while maintaining strong security guarantees through a custom authentication mechanism using WebSockets and one-time install-time verification. The architecture separates concerns between a central server, which interfaces with the orchestrator, and lightweight agents deployed in remote networks that manage provisioning via Ubuntu MAAS. This approach avoids exposing remote environments directly to the internet, reducing attack surface and simplifying coordination across multiple sites. Evaluation results demonstrate significant improvements in both deployment speed (94% time reduction for bare metal deployment and 60% for VM) and consistency compared to manual provisioning processes, validating the effectiveness of the automated approach for large-scale edge infrastructure deployment.

Background/Objective: Predictive models for cardiovascular disease rely predominantly on electronic health records (EHRs), whereas wearable devices, home monitoring, and patient-reported outcomes offer new opportunities for multimodal risk prediction; however, integrating these heterogeneous sources into a reproducible, clinically usable machine learning workflow remains challenging. This study aimed to develop an end-to-end architecture for explainable multimodal time-series prediction in patients with heart failure (HF), left ventricular assist device (LVAD) recipients, and heart transplant (HT) recipients. Methods: The modular architecture harmonizes Health Level Seven Fast Healthcare Interoperability Resources (HL7 FHIR) and non-FHIR telemonitoring data into a unified event log, which leakage-aware temporal preprocessing converts into patient-day feature matrices. Configurable adverse-event tasks (HF hospitalization, cardiac decompensation, transplant rejection, and mortality) are modeled using gradient-boosted trees or temporal neural networks, with optional probability calibration. Explanations combine SHapley Additive exPlanations (SHAP) for tabular models with temporal-perturbation attribution and integrated gradients for sequence models. The pipeline is declaratively configured, tracked with MLflow, and deployable via command-line and web interfaces. It was developed alongside the prospective multicenter RETENTION trial of a telemonitoring-based care pathway and was subsequently applied to data collected within the trial. Results: Applied to the RETENTION cohort, the framework executed the complete workflow, from heterogeneous source data to per-patient-day probability estimates for each prediction task, with feature- and time-level explanations presented as clinician-facing narratives. The cohort served to demonstrate the architecture, not to establish clinical predictive performance or prospective clinical validation. Conclusions: This study provides a reproducible, end-to-end architecture that unifies data harmonization, temporal processing, machine learning orchestration, explainability, and deployment for multimodal clinical prediction. Its modular, disease-agnostic design supports adaptation to other diseases, settings, and data sources, laying a foundation for prospective validation and clinical translation.