Industry 5.0 improves human-machine collaboration by integrating Artificial Intelligence (AI) and Extended Reality (XR) into industrial environments. This paper presents a structured Reference Architecture for a system that addresses industrial environments within the Industry 5.0 paradigm, iteratively refined based on evolving business and technical requirements. Designed for scalability, interoperability, and flexibility, it enables seamless interaction between XR applications, AI-powered decision support tools, and industrial systems. The architecture incorporates an enterprise architecture perspective that addresses industry-driven use cases and user scenarios from six pilot applications. Key components, including XR applications, AI recognition models, digital twins (DTs), and an orchestration hub, are outlined. The business layer is built on user stories that reflect real-world industrial needs from various sectors, and future directions for extending the architecture within Industry 5.0 ecosystems are discussed.

As food safety is undergoing through significant challenges due to recent food scandals, and the consumers demands for products of higher quality is increasing, the need for better knowledge of the food production processes and adoption of data sharing practices in the product and supply chain management are emerging. To address those issues, data sharing platforms have been introduced as essential tools for creating high value from data with secure and mutually beneficial multi-partner data sharing fascilitation. Blockchain technology, through its inhereted distributed nature can help to build trust mechanisms to enhance transparency and security dimension of food chains. In this work we propose a novel security mechanism for proper authentication and authorization when accessing resources through an agrifood data platform. Our proposed methodology aims to deliver sophisticated backbone service capabilities that will enable trusted, secure, automated, robust and controlled data transactions for food certification to all food sector businesses that demand easy, fast, and actionable access to variegating food safety data from multiple devices and in various settings.

Artificial Intelligence (AI) advances are pushing the boundaries across research domains with AI-driven solutions in healthcare claiming a significant share. A key objective of these studies concerns the timely prediction of various pathological conditions. Sepsis is a life-threatening syndrome and one of the main causes of death in intensive care unit (ICU) patients. As it becomes a major health problem worldwide, sepsis early prediction could assist healthcare professionals towards making informed clinical decisions, and thereby, significantly reducing the sepsis' morbidity and mortality. A notable body of literature involving the use of AI for sepsis prediction exists. However, to the best of our knowledge, only a handful of studies focus on performing a systematic review of the AI enabled solutions for sepsis prediction in ICUs. In this context, the present paper aims to identify knowledge gaps, stimulate interest and yield motivations for future research. Moreover, to discuss ethical and explainability aspects and associated challenges. The literature search was conducted between February 2023 and April 2023 and considered eligible articles published within the last five years.

The emergence of the Industrial Internet of Things paves the way for enhancing the real-time monitoring capabilities of contemporary manufacturing enterprises through the extensive utilization of physical and virtual sensors. This paradigm enables the detection of early warning signals concerning systems' degradation and facilitates the prompt decision making and actions performed ahead of time. Currently, even large manufacturing companies have not yet developed a complete Predictive Maintenance strategy and appropriate sensor-driven, real-time systems in order to utilize these benefits. In this paper, we propose a failure prediction system for complex IT systems in the steel industry. The novelty of our work lies in the exploitation of Deep Learning techniques from streaming operational sensor data, enabling earlier failure predictions through a Neural Networks approach. To evaluate the proposed framework, real-life data are collected and analyzed based on daily operational and maintenance activities within the production line. We further demonstrate the framework's potential by presenting some early results in modeling and predicting the complex and dynamic behavior in the manufacturing settings.

The increase in the volume and heterogeneity of biomedical data sources has motivated researchers to embrace Linked Data (LD) technologies to solve the ensuing integration challenges and enhance information discovery. As an integral part of the EU GRANATUM project, a Linked Biomedical Dataspace (LBDS) was developed to semantically interlink data from multiple sources and augment the design of in silico experiments for cancer chemoprevention drug discovery. The different components of the LBDS facilitate both the bioinformaticians and the biomedical researchers to publish, link, query and visually explore the heterogeneous datasets. We have extensively evaluated the usability of the entire platform. In this paper, we showcase three different workflows depicting real-world scenarios on the use of LBDS by the domain users to intuitively retrieve meaningful information from the integrated sources. We report the important lessons that we learned through the challenges encountered and our accumulated experience during the collaborative processes which would make it easier for LD practitioners to create such dataspaces in other domains. We also provide a concise set of generic recommendations to develop LD platforms useful for drug discovery.

In this paper, an architecture is presented that allows the extraction of argumentation clauses that might exist in publications, in order to perform molecular clustering on referenced molecules. Grammar rules are defined and used to identify sentences corresponding to argumentation being present in publications. The references of those molecules are then compiled as lists that include their structure definition in SMILES format. These lists are given as input to virtual screening tools and then to a molecular clustering tool, with the ultimate goal to classify molecules that are known to be prone to specific diseases, thus leading to the discovery of new drugs.

Electronic Health Records (EHRs) contain an increasing wealth of medical information, which has the potential to significantly advance medical research and health policies formulation, providing society with additional benefits within a global health perspective. However, the European healthcare information space is fragmented due to the lack of legal and technical standards, cost effective platforms, and sustainable business models. Providing an interoperability infrastructure for EHRs is on the agenda of many regional, and international eHealth initiatives. The semantic interoperability of patient data between EHRs and medical research can transform today's process of drug discovery and development, enable faster access to effective new medications, provide improved patient outcomes, and provide a key foundation for targeted (personalized) medicines. The scope of the current paper is the description of the effort undertaken by the Linked2Safety consortium towards the development of an innovative interoperability framework, for the efficient, homogenized access to and the effective utilization of the increasing wealth of medical information contained in the EHR systems deployed and maintained at regional and/or national level across Europe.

A set of well-integrated clinical terminologies is at the core of delivering an efficient clinical trial system. The design and outcomes of a clinical trial can be improved significantly through an unambiguous and consistent set of clinical terminologies used in a participating clinical institute. However, due to lack of generalised legal and technical standards, heterogeneity exists between prominent clinical terminologies as well as within and between clinical systems at several levels, e.g., data, schema, and medical codes. This article specifically addresses the problem of integrating local or proprietary clinical terminologies with the globally defined universal concepts or terminologies. To deal with the problem of ambiguous, inconsistent, and overlapping clinical terminologies, domain and knowledge representation specialists have been repeatedly advocated the use of formal ontologies. We address two key challenges in developing an ontology-based clinical terminology (1) an ontology building methodology for clinical terminologies that are separated in global and local layers, and (2) aligning global and local clinical terminologies. We present Semantic Electronic Health Record (SEHR) ontology that covers multiple sub-domains of Healthcare and Life Sciences (HCLS) through specialisation of the upper-level Basic Formal Ontology (BFO). One of the main features of SEHR is layering and adaptation of local clinical terminologies with the upper-level BFO. Our empirical evaluation shows an agreement of clinical experts confirming SEHR's usability in clinical trials.

This paper proposes a Bayesian association rule mining algorithm (BAR) which combines the Apriori association rule mining algorithm with Bayesian networks. Two interesting-ness measures of association rules: Bayesian confidence (BC) and Bayesian lift (BL) which measure conditional dependence and independence relationships between items are defined based on the joint probabilities represented by the Bayesian networks of association rules. BAR outputs best rules according to BC and BL. BAR is evaluated for its performance using two anonymized clinical phenotype datasets from the UCI Repository: Thyroid disease and Diabetes. The results show that BAR is capable of finding the best rules which have the highest BC, BL and very high support, confidence and lift.