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The shop floor or factory floor is the area inside a factory where manufacturing production is executed. The digitalisation of this area has been increasing in the last few years, introducing the Digital Twin (DT) and the Industry 4.0 concepts. A DT is the digital representation of a real object or an entire system. A DT includes a high diversity of components from different vendors that need to interact with each other efficiently. In most cases, the development of standards and protocols does not consider the need to operate with other standards and protocols, causing interoperability issues. Transducers (sensors and actuators) use the communication layer to exchange information with digital contra parts, and for this reason, the communication layer is one of the most relevant aspects of development. This paper covers DT development, going from the physical to the visualisation layer. The reference architecture models, standards, and protocols focus on interoperability to reach a syntactic level of communication between the IEEE 1451 and the IEC 61499 standards. A semantic communication layer connects transducer devices to the digital representation, achieving a semantic level of interoperability. This communication layer adds semantics to the communication process, allowing the development of an interoperable DT based on the IEEE 1451 standards. The DT presented reaches the syntactic and semantic levels of interoperability, allowing the monitoring and visualisation of a prototype system.

According to the FAIR guiding principles, one of the central attributes for maximizing the added value of information artifacts is interoperability. In this paper, I discuss the importance, and propose a characterization of the notion of . Moreover, I show that a direct consequence of this view is that Semantic Interoperability cannot be achieved without the support of, on one hand, (i) , as capturing the conceptualizations represented in information artifacts and, on the other hand, of (ii) , as a discipline proposing formal meth- ods and theories for clarifying these conceptualizations and articulating their representations. In particular, I discuss the fundamental role of formal ontological theories (in the latter sense) to properly ground the construction of representation languages, as well as methodological and computational tools for supporting the engineering of (in the former sense) in the context of FAIR.

The integration and exchange of information among health organizations and system providers are currently regarded as a challenge. Each organization usually has an internal ecosystem and a proprietary way to store electronic health records of the patient’s history. Recent research explores the advantages of an integrated ecosystem by exchanging information between the different inpatient care actors. Many efforts seek quality in health care, economy, and sustainability in process management. Some examples are reducing medical errors, disease control and monitoring, individualized patient care, and avoiding duplicate and fragmented entries in the electronic medical record. Likewise, some studies showed technologies to achieve this goal effectively and efficiently, with the ability to interoperate data, allowing the interpretation and use of health information. To that end, semantic interoperability aims to share data among all the sectors in the organization, clinicians, nurses, lab, the entire hospital. Therefore, avoiding data silos and keep data regardless of vendors, to exchange the information across organizational boundaries. This study presents a comprehensive systematic literature review of semantic interoperability in electronic health records. We searched seven databases of articles published between 2010 to September 2020. We showed the most chosen scenarios, technologies, and tools employed to solve interoperability problems, and we propose a taxonomy around semantic interoperability in health records. Also, we presented the main approaches to solve the exchange problem of legacy and heterogeneous data across healthcare organizations.

Pervasive healthcare services have undergone a great evolution in recent years. The technological development of communication networks, including the Internet, sensor networks, and M2M (Machine-to-Machine) have given rise to new architectures, applications, and standards related to addressing almost all current e-health challenges. Among the standards, the importance of OpenEHR has been recognized, since it enables the separation of medical semantics from data representation of electronic health records. However, it does not meet the requirements related to interoperability of e-health devices in M2M networks, or in the Internet of Things (IoT) scenarios. Moreover, the lack of interoperability hampers the application of new data-processing techniques, such as data mining and online analytical processing, due to the heterogeneity of the data and the sources. This article proposes an Internet of Medical Things (IoMT) platform for pervasive healthcare that ensures interoperability, quality of the detection process, and scalability in an M2M-based architecture, and provides functionalities for the processing of high volumes of data, knowledge extraction, and common healthcare services. The platform uses the semantics described in OpenEHR for both data quality evaluation and standardization of healthcare data stored by the association of IoMT devices and observations defined in OpenEHR. Moreover, it enables the application of big data techniques and online analytic processing (OLAP) through Hadoop Map/Reduce and content-sharing through fast healthcare interoperability resource (FHIR) application programming interfaces (APIs).

With the constant growth of Internet of Things (IoT) ecosystems, allowing them to interact transparently has become a major issue for both the research and the software development communities. In this paper we propose a novel approach that builds semantically interoperable ecosystems of IoT devices. The approach provides a SPARQL query-based mechanism to transparently discover and access IoT devices that publish heterogeneous data. The approach was evaluated in order to prove that it provides complete and correct answers without affecting the response time and that it scales linearly in large ecosystems.

In the era of Industry 4.0, pervasive adoption of communication technologies based on the Internet of Things represents a very strong requirement in several domains. In the smart grid domain, there is the need to overcome one of the main limitations of the current electric grid, allowing the use of heterogeneous devices capable of measuring, monitoring and exchanging information about grid components. For this reason, current literature often presents research activities about enabling internet of things (IoT) in smart grids; in particular, several proposals aim to realize interworking between IoT and smart grid communication standards, allowing exchange of information between IoT devices and the electrical grid components. Semantic interoperability should be achieved in an interworking solution in order to provide a common meaning of the data exchanged by heterogeneous devices, even if they belong to different domains. Until now, semantic interoperability remains an open challenge in the smart grid field. The paper aims to propose a novel solution of interworking between two of the most used communication systems in smart grids and IoT domains, i.e., IEC 61850 and oneM2M, respectively. A semantic interoperability solution is also proposed to be used in the interworking scheme here presented.

Metadata, data about other digital objects, play an important role in FAIR with a direct relation to all FAIR principles. In this paper we present and discuss the FAIR Data Point (FDP), a software architecture aiming to define a common approach to publish semantically-rich and machine-actionable metadata according to the FAIR principles. We present the core components and features of the FDP, its approach to metadata provision, the criteria to evaluate whether an application adheres to the FDP specifications and the service to register, index and allow users to search for metadata content of available FDPs.

Over the past decade, trait data collection and mobilisation have expanded significantly, yet much of this data remains only partially compliant with FAIR principles. A major challenge lies in the inconsistent use of standards for harmonising heterogeneous trait data, along with the diversity, redundancy, and poor alignment of semantic artefacts developed to address this challenge. This study presents an approach to enhance the interoperability of the Trait Thesauri developed within the LifeWatch Italy research infrastructure for annotating and standardising trait data and metadata of aquatic organisms. This approach combines manual and automated alignment techniques, tested within the 2023 Ontology Alignment Evaluation Initiative. Domain experts manually aligned the Phytoplankton, Zooplankton, Macroalgae, Macrozoobenthos, and Fish trait thesauri, while five matching tools, LogMap, LogMapKG, LogMapLt, Matcha, and OLaLa, were tested for automated mappings. Both approaches revealed significant overlap among thesauri: Manual mapping identified 160 cross-thesauri correspondences and served as a benchmark for evaluating automated matching systems. Automated tools showed variable performance, with OLaLa achieving the best automated alignment results, with an F-measure of 0.93. Challenges in alignment included varying linguistic expressions and differing levels of concept specificity. The results highlight the importance of combining automation with expert validation to ensure mapping quality and allowed the development of a unified Trait Thesaurus, which integrates approximately 500 harmonised concepts, reducing redundancy and enhancing FAIR compliance in ecological and trait-based research.

In recent years, Internet-of-Things (IoT)-based applications have been used in various domains such as health, industry and agriculture. Considerable amounts of data in diverse formats are collected from wireless sensor networks (WSNs) integrated into IoT devices. Semantic interoperability of data gathered from IoT devices is generally being carried out using existing sensor ontologies. However, crop-specific trait ontologies—which include site-specific parameters concerning hazelnut as a particular agricultural product—can be used to make links between domain-specific variables and sensor measurement values as well. This research seeks to address how to use crop-specific trait ontologies for linking site-specific parameters to sensor measurement values. A data-integration approach for semantic and syntactic interoperability is proposed to achieve this objective. An open-data platform is developed and its usability is evaluated to justify the viability of the proposed approach. Furthermore, this research shows how to use web services and APIs to carry out the syntactic interoperability of sensor data in agriculture domain.

Quality indicators (QIs) can help assess intensive care quality, identify potential for improvement, and ultimately enhance patient outcomes. Therefore, the German Interdisciplinary Association of Critical Care and Emergency Medicine (DIVI) has developed QIs for intensive care medicine. However, variability in how these are technically implemented across health care facilities currently limits their comparability. The aim of the study is to develop unambiguous computer-interpretable representations of the DIVI QIs for intensive care medicine using Fast Healthcare Interoperability Resources (FHIR) and to establish a replicable process for translating narrative QIs into standardized digital formats. We first decomposed the narrative DIVI intensive care medicine QIs into two sets of semantic concepts that characterize (1) the targeted patient population and (2) the care aspect specified by each indicator. We mapped the concepts to international vocabularies, defining a supplementary code system for concepts not appropriately represented in existing vocabularies. The decomposed and semantically mapped QIs were then implemented in FHIR using an implementation guide we previously developed to represent clinical practice guideline recommendations. As the translation process holds risks of inducing logical and semantic deviations, the final FHIR representations were back-translated into a narrative form and reviewed with clinical experts, including the authors of the original QIs. The decomposition and semantic mapping were iteratively adjusted based on the experts' feedback until the results accurately reflected the original intent of the QIs. The 10 DIVI QIs were decomposed into 31 separately measurable indicators, including 9 structural indicators, 17 process indicators, and 5 outcome indicators. All process and outcome indicators were successfully specified as computer-interpretable representations in FHIR. In total, 58 unique medical concepts were used, of which 52 (90%) could be mapped to concepts from international vocabularies. The remaining 6 concepts-mostly intensive care unit-specific scores or roles-were defined in a supplementary code system. Nested Boolean logic and temporal conditions were fully supported using standard FHIR mechanisms. After iterative adjustments, the final representations were approved as accurate representations of the DIVI QIs by the clinical expert panel. Our work demonstrates that the structured process developed here enables the unambiguous, computer-interpretable representation of QIs for intensive care. These representations can be used in automated quality management systems to standardize quality assessments across health care facilities. Our newly defined structured process can serve as a blueprint for similar efforts in other specialties. The here-developed computer-interpretable QIs are openly available for reuse and ongoing maintenance. Future work will focus on piloting these indicators in real-world clinical systems and extending the framework to include structural indicators.