Workers in hazardous and extreme environments, such as confined spaces, offshore platforms, or isolated ICE (Isolated, Confined, and Extreme) settings, face significant physical, physiological, and psychological stressors that threaten performance, safety, and long-term well-being. This short survey, inspired by Industry 5.0’s human-centered principles, categorizes the main sources of strain in these workplaces and examines their immediate and lasting impacts on health and productivity. It then explores how Virtual Reality (VR) technologies can help mitigate these challenges through immersive simulations, targeted psychological training, and real-time remote support. By integrating VR into preparation and ongoing operations, organizations can foster safer, more supportive workplaces that prioritize both performance excellence and the dignity, mental health, and resilience of workers.

The industrial metaverse represents a significant advancement towards Industry 5.0, enabling improvements in efficiency, training, and remote collaboration within industrial contexts. Despite its transformative potential, widespread adoption faces substantial challenges, particularly cybersickness, a condition characterized by nausea, dizziness, and disorientation arising from prolonged exposure to virtual environments. Current cybersickness mitigation strategies often lack personalization, failing to accommodate individual differences such as motion sensitivity and prior experiences. In order to contribute to address this gap, this paper introduces a novel Personal Digital Twin (PDT)-driven approach that aims to mitigate personalized cybersickness by integrating real-time biometric data monitoring, individualized susceptibility assessment, and dynamic XR environment adaptation. The authors also validated the proposed approach by leveraging two concrete industrial case studies: the offshore wind turbine maintenance and the overhead crane operations training. A significant result of the research work is that a PDT-driven adaptive training can contribute to enhance user comfort, reduce cybersickness, and promote more effective and inclusive adoption of industrial metaverse applications.

The article emphasizes the critical importance of language generation today, particularly focusing on three key aspects: Multitasking, Multilinguality, and Multimodality, which are pivotal for the Natural Language Generation community. It delves into the activities conducted within the Multi3Generation COST Action (CA18231) and discusses current trends and future perspectives in language generation.

The article emphasizes the critical importance of language generation today, particularly focusing on three key aspects: Multitasking, Multilinguality, and Multimodality, which are pivotal for the Natural Language Generation community. It delves into the activities conducted within the Multi3Generation COST Action (CA18231) and discusses current trends and future perspectives in language generation.

The problem of achieving a good maintenance plan is well-known in the modern industry. One of the most promising approaches is predictive maintenance, which schedules interventions based on predictions made by collecting and analyzing data from the process. However, to the best of the authors’ knowledge, this approach is still not widespread and known enough, and particularly, the real-case scenarios of its application appear not exhaustive. To contribute to fill this gap, this work proposes a digital twin (DT), which performs a predictive maintenance approach for a conveyor belt within a real-case scenario with the overall goal of predicting faults during normal belt operations. Specifically, the core of the implemented DT is a model that analyzes the data collected by various sensors distributed along the conveyor belt. In turn, this model exploits a machine learning-based algorithm that predicts the insurgence of faults. The tests of the developed solution, conducted within a real scenario, demonstrated good precision and accuracy in identifying the fault status and also in a time deemed acceptable for the involved stakeholders.

The purpose of this article is to highlight the critical importance of language generation today. In particular, language generation is explored from the following three aspects: multi-modality, multilinguality, which play crucial role for NLG community. We present the activities conducted within the Multi3Generation COST Action (CA18231), as well as current trends and future perspectives for multitask, multilingual and multimodal language generation.

This work presents a digital-twin-based framework focused on orchestrating human-centered processes toward Industry 5.0. By including workers and their digital replicas in the loop of the digital twin, the proposed framework extends the traditional model of the factory's digital twin, which instead does not adequately consider the human component. The overall goal of the authors is to provide a reference architecture to manufacturing companies for a digital-twin-based platform that promotes harmonization and orchestration between humans and (physical and virtual) machines through the monitoring, simulation, and optimization of their interactions. In addition, the platform enhances the interactions of the stakeholders with the digital twin, considering that the latter cannot always be fully autonomous, and it can require human intervention. The paper also presents an implemented scenario adhering to the proposed framework's specifications, which is also validated with a real case study set in a factory plant that produces wooden furniture, thus demonstrating the validity of the overall proposed approach.

Over the years, the importance of considering human needs in every aspect of our life has been rising. This evolution is signing the birth of a new era of Industry, called Industry 5.0. While in all Industry Eras the human role has always been overlooked, prioritizing efficiency and production, with the birth of industry 5.0 there is a new paradigm shift, which sees humans needs and capabilities at the center of the process and values. It is in this context that this paper takes place. We tried to identify and study the interactions of all the stakeholders involved in an assembly line of a sofa factory and provided an interactive map based on Real Time Location System technology. In particular, this interactive map can inform about the location and status of various assets of the industry, aiming at contributing to defining and implementing human-centricity in the assembly line.

In recent years, the interest in miniaturization of devices easier to wear or even to insert into the body and in general the interest in building lightweight and compact systems are greatly increased. For this reason, the field of micro manufacturing is becoming more and more strategic for modern industry. One critical aspect for the adoption of micro manufacturing technologies on large scale is the quality validation and metrology of components or devices with conventional technologies, such as vision systems or tactile profilometer. Indeed, these technologies hardly fit to be used on the micro devices due to their extremely reduced dimensions which cause a high risk of damaging the micro structured surface. For these reasons, one of the challenges in the micro manufacturing field is the study of methods and tools for the continuous monitoring of the micro production process, with the final aim to improve reliability. In this regard, this paper focuses on a typical micro manufacturing technology, such as micro injection moulding, and presents a methodological approach that can help companies to adopt a solution for optimizing their process leveraging the corresponding Digital Twin. The latter represents a mirror of the physical process that allows to monitor in-line its parameters, to compare them with any analytic model, and to supply specific varia-tions of parameters to keep them always in optimal conditions. The paper also presents a proof of concept of the proposed approach that has been validated to prove the correctness and its capability to scale to a real case study. (C) 2021 Elsevier B.V. All rights reserved.

The growing relevance of digitalization in production requires the enhancement of human skills and competences in the field of Information and Communication Technology (ICT). Higher education has to cope with this need by providing the necessary ICT skills to future industrial engineers, so that they have a good understanding of the complexity of industries in the 21st century. This paper presents the conceptual development and testing of a Virtual Learning Factory Toolkit (VLFT) that integrates digital tools used in production management with engineering education. The digital tools integrated into the VLFT can help students to exploit enabling technologies such as simulation and virtual reality in their manufacturing studies and practical projects with industrial companies. Moreover, digital tools were tested by using a structured workflow that consists of different learning activities related to manufacturing system configuration. Students practised the digital tools with the help of use cases in the form of joint learning labs, after which the students' feedback was collected and analysed.

Technologies for the effective and efficient handling of RDF data are one of the main success factors for a larger scale take-up of Semantic Web Technologies in real scenarios. In this regard, several software components (RDF Stores) devoted to the semantic data persistence and retrieval are available in literature. However, each of them may be appropriate and usable for some kinds of tasks and not for others, and a one-size-fits-all killer application for this type of solutions is still not (and probably will never be) available. The large number of available solutions and the lack of widely accepted benchmarks for their rigorous evaluation do not help the selection and the adoption of an appropriate RDF store compliant with the identified needs of a specific case study. In order to contribute to fill this gap, a methodological approach to evaluate and rank the relevant features of the RDF stores is presented in this paper. Such an approach can help on one hand other researchers to discover the factors affecting the success of the RDF stores and the other hand software architects to select which RDF stores best fits the requirements of a certain application scenario.

The rise of Internet of Things (IoT) technologies provided the means to enable the smart home (SH), a residence aimed at anticipating and responding to its dwellers’ needs and one of the most promising Ambient Assisted Living (AAL) solutions. However, the massive adoption of monitoring techniques and some shortcomings in the field of security can hinder the adoption of IoT-based SH solutions. In this work, we describe how we have addressed the challenges arising from the fields of AAL and continuity of care, by creating the Smart Human-Centred Living Environment Lab, an innovative prototype of a SH that can address the specificities of each inhabitant. The system on which the SH relies is a knowledge-based system that leverages the semantic representations of relevant concepts and data, thus allowing for the customization of services according to each inhabitant’s needs and preferences, without the need of continuous monitoring. A semantic middleware ensures the semantic interoperability of the information and thus the realization of an IoT-based architecture optimizing the provision of each service. Finally, digital applications and virtual reality-based systems are integrated in the SH to provide support to the activities of daily living and the execution of rehabilitative exercises, in the perspective of continuity of care. The system tries to address the issues of other SHs, in which privacy concerns, stigma, and ageism may hinder the use of innovative technologies in daily life. However, a few issues still remain: among these, the validation of the whole system in terms of users’ acceptance and the possibility of providing such a service on a large scale.

CasAware is an Ambient Assisted Living platform, developed within an Italian research project, with the aim to improve the level of comfort and well-being of inhabitants of a house, while optimizing the energy consumption. A key feature, for successful realization of such a platform, is its capability to interoperate with other IoT platforms, which can augment CasAware with additional services. Indeed, this capability facilitates smooth communication between CasAware devices and external devices connected to other IoT platforms, thus allowing efficient exchange of messages among them. However, such integration is hindered by the heterogeneity of data models used in different platforms, which is also related to lack of common standards. In order to realize integration needed for CasAware, this paper presents an approach which exploits results of the INTER-IoT project. Specifically, the INTER-IoT methodology and a set of software tools for achieving IoT interoperability are applied. In the presented study, it is shown how the INTER-IoT based approach can facilitate interoperability between CasAware and two other platforms, which use completely different data models.

CasAware is an Ambient-Assisted Living platform, which aims at improving level of comfort and well-being of inhabitants, while optimizing energy consumption. A key feature for a successful realization of such a platform is its integration with other available/deployed IoT solutions. Indeed, this integration has to facilitate smooth communication between the CasAware platform and devices, and other IoT devices, in particular to enable the exchange of data sets among them. In this paper, we introduce an approach, followed in CasAware, to realize such integration. Specifically, the proposed solution exploits the guidelines of the INTER-IoT project, which proposed a framework for inter-platform communication. So far, various existing IoT platforms have been plugged into this framework, originating from multiple application fields, thus demonstrating the advantages of such integration, capable of disregarding the specific application context. The idea behind the herein presented study is that an INTER-IoT-based approach can guarantee enhancement of interoperability between CasAware and other platforms, thus promoting a unified view of the data, from client’s perspective, within the complete IoT ecosystem.

Industry 4.0 paradigm envisions a new generation of collaborative manufacturing system where all the components are connected exploiting the Industrial Internet of Things (IIoT) protocol. Through this pervasive connection, sensors, machines, robots and other production equipment can communicate and share information with their surroundings resources. In order to contribute to realise such a vision, this paper introduces an event-driven framework that enables the interaction of heterogeneous distributed resources, according to the principles of Industry 4.0 paradigm. The overall aim of this research is providing new mechanisms to notify significant information produced within the factory towards enabled and interested production resources. In particular, combining various technologies such as IIoT, Semantic Web, and Multi-Agent based systems, the proposed framework allows the resources to be updated about changes occurred in their context. Under these conditions, the framework can be exploited in all the scenarios where the cooperation among resources is a strict requirement, thus contributing to support various ad-hoc services that drive modern factories towards a more sustainable, efficient, and competitive manufacturing system. Moreover, the validity of the framework is demonstrated leveraging the implementation of its instance within a real case study.

The critical success factor of the supply chain management process in a modern manufacturing company consists in the company's capability to exploit the data produced by a growing number of different sources. The latter include a network of collaborative sensors, digital tools, and services, made available to suppliers and other involved supply chain actors by the recent advancements in digitalization. The collected data can be processed and analyzed in near real time to extract significant information useful for the company to take some relevant decisions. However, these data are typically produced under the form of heterogeneous formats, as they arrive from different types of sources. This is the reason why the real challenge is finding valid solutions that support the data integration. In this regard, this paper investigates the potential of a solution for data integration that allows supporting a set of interacting decision-support tools within the inbound logistics of the automotive manufacturing. This solution is based on a message-oriented middleware which enables a collaborative approach where suppliers, trucks, dock managers and production plants can share information about their own status for the optimization of the overall system.

The Digital Twin is a representation of characteristics and behavior of a factory according to various levels of detail and the scope it addresses. Its full range of capabilities can be exploited when it is synchronized with the real world. Indeed, in this case, it can be used to mirror the real operating conditions for simulating the real-time behavior, and thus forecasting factory performances. However, we are still far from its large-scale diffusion. The purpose of this work is to analyze both the major challenges that still have to be faced and some potential solutions for each of the identified challenges.

Digital twin (DT) is the virtual clone of a factory representing its static and dynamic aspects (e.g., processes, systems, products, etc.) in detail. Among the significant challenges that manufacturing company has to face to implement the DT, one of the most demanding is applying an appropriate software infrastructure, which would enable synchronization of the physical factory with its DT. In this case, it was possible to exploit wide range of capabilities of DT in its full potential. In particular, the DT was used in different conditions to enable various operations within the shop floor, to simulate and assess the factory's performance. To support companies in addressing this challenge, this paper presents a potential solution, based on the Industrial Internet of Things (IIoT) middleware, that implements a fully dual-way synchronization between the real and virtual worlds. A case study was carried out to investigate the possibilities to implement the solution. To demonstrate correctness and validity of the approach, tests were carried out in the laboratories of Flexible Manufacturing Systems, Robotics Demo Centre and ProtoLab of Tallinn University of Technology (TalTech).

The CasAware research project combines context awareness with ambient assisted living by using a semantic-based framework that enables the smart home paradigm for everybody.