University of Bologna

Superconducting Fault Current Limiters (SFCLs) have reached a mature Technology Readiness Level, making their deployment in power grids increasingly relevant. While applications in AC networks are well studied, HVDC grids pose distinct chal-lenges due to different fault dynamics and the reliance on Modular Multilevel Converters (MMCs). This work develops a mathematical model of a simplified DC grid including an SFCL based on High-Temperature Superconducting (HTS) tapes and an MMC repre-sented by an Averaged Value Model (AVM). The model couples net-work and converter dynamics with the nonlinear temperature- and current-dependent properties of the SFCL, enabling efficient para-metric studies under fault conditions. The analysis investigates key design parameters, such as the HTS tape length, highlighting their influence on SFCL and grid performance and providing guidelines for optimal device design.

• Enable patient digital twin deployment via a knowledge-driven, modular framework • Provide clinical data access and simulation through a FHIR-compliant API • Publish MIMO ontology to standardize and increase transparency of AI model interfaces • Automate AI model integration using a manifest-based binding protocol • Benchmark stroke risk models with real-time, time-aware clinical data streams Background: The healthcare sector faces diverse challenges, including poor interoperability and a lack of personalized approaches, which limit patient outcomes. Ineffective data exchange and one-size-fits-all treatments fail to meet individual needs. Emerging technologies like digital twins (DTs), the semantic web, and AI show promise in tackling these obstacles. For this reason, we introduced CONNECTED, a conceptual multi-level framework that combines these techniques to deploy general-purpose patient DTs. Objective: This study assesses CONNECTED’s comprehensiveness, applicability, and utility for developing intelligent, personalized healthcare applications. Specifically, we deliver a preliminary version of the framework to predict future patient states and demonstrate its automation benefits in deploying semantically enriched, AI-powered patient DTs. Methods: We enhanced the CONNECTED architecture by providing a formal definition of DT and modularizing its core functionalities into microservices—Properties, State, Capabilities, and Manifest. The Manifest service facilitates AI model integration through the Model Interface Manifest Ontology (MIMO), enabling automatic data-to-model binding via a reasoner. Using the HeartBeatKG quality assessment tool, we validated MIMO and tested the internal logic by integrating a well-established stroke-risk model. Results: Our implementation comprehends: (1) deploying a FHIR-compliant, patient-centric API for clinical history access, real-time monitoring, and predictive simulation; (2) publishing MIMO; (3) establishing the Manifest protocol for seamless, general-purpose AI model integration tailored to individual patient profiles; and (4) a proof-of-concept benchmarking application comparing multiple stroke risk classifiers. Conclusion: CONNECTED establishes a flexible, scalable foundation for interoperable semantic patient DTs. Automation reduces technical overhead and enables users to focus on delivering personalized, insight-driven care.

To explore the adoption and practical implementation of the three major health data standards (i.e., FHIR, OMOP-CDM, and openEHR), to evaluate their maturity level in terms of how extensively they have been applied and integrated into everyday clinical and research practice. We conducted a systematic review registered in PROSPERO (CRD42024623398) following PRISMA guidelines. Literature searches were performed through PubMed, Cochrane, Scopus, Web of Science, and IEEE Xplore from 2021 to 2024. After de-duplication and screening, 99 studies were included. Data was extracted and classified according to five health application domains and five use cases based on the intended purpose of the standard in the work. Studies were assessed for implementation scale, ETL tools, coverage of the standard (i.e., the number of mapped source variables), and whether standards were adapted or used as-is. Of the 99 included studies, 57% used OMOP-CDM, 39% FHIR, and 8% openEHR. Most applications occurred in research settings (87%) and focused on data reuse (47%) or clinical decision support (23%). OMOP-CDM was preferred for large-scale, longitudinal research, while FHIR was dominant in the public health domain and for real-time data exchange. Only 27% of studies reported the coverage of the standard. FHIR implementations often require customization, complicating interoperability. OMOP-CDM offered strong analytical tooling but posed challenges for mapping and data loss. Few studies using openEHR reported limitations, with its uptake remaining limited. Although FHIR, OMOP-CDM, and openEHR hold significant potential to enhance interoperability, their adoption remains fragmented. Each standard shows specific strengths: FHIR for exchange, OMOP-CDM for analytics, and openEHR for data persistence. A hybrid approach and clearer implementation practices are essential to support scalable, interoperable health data ecosystems.

• Nursing Outpatient Clinics improve adherence to diabetes clinical guidelines. • Patients with nursing support show a 9–13% improvement in follow-up testing. • These results provide key insights for policymakers designing diabetes programs. A growing share of the global population is affected by chronic conditions, prompting the development of various organizational models for chronic disease management. These models often emphasize the pivotal role of General Practitioners (GPs) and community health nurses in leading chronic care efforts. Recognizing the significant burden of diabetes as a leading cause of disability and mortality, the Emilia-Romagna Region of Italy introduced clinical guidelines for diabetes management in 2010 to enhance the quality of care for patients with type 2 diabetes. This initiative enables GPs working within Community Health Homes to collaborate with Nursing Outpatient Clinics (NOCs) for more effective chronic disease management. This study evaluates the impact of NOCs on key indicators of patient adherence with clinical guidelines. Using a balanced panel of administrative, individual-level data, we analyse the diabetic population over 65 years old within the largest Local Health Authority in Emilia-Romagna over a seven-year period (2010–2016). To assess the impact of NOCs on patient adherence, we employ alternative difference-in-differences approaches while accounting for heterogeneous treatment effects due to varying patient exposure periods. Our findings indicate that patients enrolled with GPs who integrate NOCs into diabetes management exhibit significantly improved adherence to clinical guidelines. These results offer valuable insights for policymakers designing diabetes management programs that incorporate nursing support to enhance patient engagement and adherence.

While several algorithms exist for analyzing muscle activity during sleep, none provides information on both muscle tone and movements as open-source software. We aimed to overcome this limitation by developing SOMAS (Sleep Open-source Muscle activity Analysis System). SOMAS processes European Data Format+ (EDF+) files with wake-sleep state and candidate leg movement annotations without online data sharing, quantifies muscle tone using the atonia index and the distribution of normalized electromyography values (DNE), and calculates leg movement indices based on the 2016 World Association of Sleep Medicine criteria. To demonstrate that SOMAS achieves its intended purpose, we analyzed recordings from eight patients with isolated REM sleep behavior disorder (iRBD), five with restless legs syndrome (RLS), seven with sleep breathing disorders, and five controls. SOMAS-derived atonia index and leg movement indices were compared with those from Hypnolab, a non-open access software. Additionally, SOMAS-derived indices were used to differentiate patients with iRBD or with RLS from other patients and/or controls. SOMAS-derived atonia index and leg movement indices strongly correlated with Hypnolab results (Spearman coefficients >0.97) with minimal bias. The DNE and atonia index in REM sleep effectively differentiated patients with iRBD from other patients and controls (AUC 0.89-1.00). The periodic leg movement and periodicity indices differentiated patients with RLS from controls (AUC 0.71-0.75). SOMAS reliably quantifies muscle tone and movements during sleep from EDF+files using open-source algorithms, with the potential of enhancing reproducibility and collaboration in research on sleep-related movement disorders.

The concept of sustainability, as outlined by the United Nations’ Sustainable Development Goals (SDGs), refers to the ability to meet the needs of the present without compromising the ability of future generations to meet their own needs. This vision is addressed by combining goals concerning the environmental , social , and economic spheres. In this context, Recommender Systems (RS) have emerged as tools that can foster these principles by nudging responsible user behavior and promoting sustainable decision-making. However, the interplay between RS and sustainability is inherently complex since it can be analyzed from two different perspectives: (i) RS for Sustainability , which focuses on how recommendation algorithms can support the achievement of SDGs, and (ii) Sustainability of RS , which focuses on developing recommendation models that inherently adhere to sustainability principles. While the integration of both these perspectives is beneficial and crucial, unfortunately, the current literature has addressed these aspects independently. Accordingly, in this survey, we first provide a comprehensive review of the existing literature on RS that either promotes sustainable behaviors aligned with the SDGs or embeds sustainability principles into their algorithmic design. Next, we identify current gaps and propose key research directions toward an integrated, holistic approach that concurrently addresses both aspects to advance the development of sustainable RS. • This survey distinguishes two key dimensions: Recommender Systems (RS) for Sustainability and Sustainability of RS. • The work reviews existing studies that promote sustainable behaviors or embed sustainability principles in their design. • The work identifies critical gaps in current research and outlines a roadmap for developing sustainable recommender systems.