This study examines the effects of an immersive virtual reality scenario on participants' risk perception and flood-related self-efficacy as well as its ef-fects on the act of taking precautionary measures for floods. In the short term, a significant increase in risk perception and flood-related self-efficacy was observed following the intervention, which is likely explained by cogni-tive biases, such as the availability heuristic. Participants perceived the risk of flooding more realistically, supported by the increased availability of in-formation in their memory. The risk perception and self-efficacy measures did not decline significantly after a three-month interval with participants still perceiving an enhanced ability to cope with flood events. These results suggest that the VR scenario has a sustainable impact on both risk percep-tion and self-efficacy. Moreover, mixed effects were observed regarding spe-cific precautionary measures, indicating limitations in the data collection methodology. Overall, the study demonstrates the potential of VR as an ef-fective tool in disaster training and highlights the importance of immersive, gamified learning methods for disaster preparedness.

Training new employees on high-tech, often one of a kind, manufacturing systems is a challenging task to achieve, especially without impeding the regular operational processes, as this might bring the production to a halt. Offsite training often lacks hands-on interaction with the machines and can impart advanced concepts and workflows only in a limited way. If XR is employed during such offsite trainings, the learning experience can be improved. However, such scenarios are costly to build and often limited to only the training phase but not the actual operation of the machines in the manufacturing environment. Similar challenges are also present at engineering education facilities where students are frequently trained to operate manufacturing equipment.

Combining XR technologies, digital twins and bi-directional data access enables the use of XR technologies not just for training but also the actual control and monitoring of industry grade machines, both on-site and remote. The aim of this work therefore is to devise and build such a dual-use XR application and evaluate its cost-to-build and its impact on the operators’ training and handling of the machines. Based on user studies, the intention is to derive recommendations and guidelines for the setup and deployment of such dual-use XR systems in educational as well as industrial settings.

A novel manufacturing machine for polymer injection moulding was selected as an exemplary machine. For this machine, a comprehensive XR learning and operation environment was developed to aid during all steps of training, setup and operation, thus supporting the operator throughout all stages of interaction with the machine.

CONTEXT

The monitoring of particulate matter present in cities with high population density, or with industries close to Monitoring "particulate matter" (e.g. pollutants or toxins) is essential in cities with high population density or industries close to urban centres and is a necessary response to the increasing rates of diseases associated with air quality. In this context, and framed within the study of Wireless Sensor Networks (WSN), the idea of implementing a sensor network capable of constant monitoring of "particulate matter" arises, which, through the use of AI, can generate predictions of the behaviour of this material, to generate a work plan with the corresponding authorities.

PURPOSE OR GOAL

This research aims to propose a distributed architecture design for a WSN, where the efficient use of sensor node resources (energy, messages, computation, among others) of the “particulate matter” of the environment of study. A distributed leader selection algorithm will send the data from WSN to Edge Computing to process the collected data and then send the processed information to cloud computing.

APPROACH

A simulation using OMNET++ software will present two scenarios with different behavioural algorithms. In both cases, the following parameters will be measured: correct and error measurements, messages sent, messages skipped (penalty), energy consumed, and leader selection behaviour.

ACTUAL OR ANTICIPATED OUTCOMES

The system performance is expected to improve regarding the number of valid messages sent and the energy consumption of the nodes involved.

CONCLUSIONS/RECOMMENDATIONS/SUMMARY

By implementing the correct communication algorithm, we can extend the lifespan of nodes in the WSN, enabling the network to transmit a larger volume of data. This will significantly enhance AI training and empower us to take steps toward improving air quality.

This paper presents a modular framework for enhancing the digital product passport (DPP) by incorporating a detailed account of the carbon footprint throughout the production process. Using IoT sensor technology, the system gathers time-series data on resource consumption during production. A work-flow engine coordinates the creation of the DPP, allowing for dynamic control and documentation of each step’s environmental impact. The enriched DPP is publicly accessible, providing a transparent step-by-step record of resource use and CO2 emissions, which is available to consumers via web services and QR code use. The findings demonstrate the scalability and flexibility of this system, emphasizing its applicability across various industries, such as flow production or the construction sector. The presented approach enables detailed traceability of resource consumption, allowing manufacturers to monitor usage dynamical-ly and independently of production workflows. By establishing a real-time tracking mechanism, the system ensures that each production step’s resource demand is recorded, regardless of process variations.

The paper starts with an introduction and then reviews the state of the art on product passports, Asset Administration Shell (AAS), and Cyber-Physical Pro-duction Systems (CPPS), outlines the conceptual framework, and describes the implementation and demonstrator. The conclusion summarizes the findings and provides an outlook on future advancements.

This paper presents the concept and implementation of a Virtual Reality (VR) application designed to generate interest in mechanical engineering among pre-university students and engineering freshmen. The presented application com-bines physical demonstrators with virtual interactions to create an engaging hy-brid learning experience. The focus is on creating interest rather than technical depth while maintaining correct terminology, presentation, and function. Through two progressive game modes of increasing difficulty, fundamental en-gineering concepts are experienced, particularly three-panel views and technical drawings. Implemented using the Unity 3D engine for autonomous VR head-sets, the application features multilingual support and intuitive interactions. Ini-tial testing with over 200 participants demonstrates high engagement levels and positive feedback, particularly when isometric views supplement the three-panel views. The application serves as an accessible entry point for students to engage with technical drawings and spatial thinking, contributing to the grow-ing field of gamified engineering education.