The design research and education landscapes are changing. The widespread development and use of technologies such as Mixed Reality (MR) and the diffusion of Head-Mounted Displays (HMDs) available at low cost are causing a shift in design education toward the Metaverse. In this ever-changing scenario, there is a need to rethink design and teaching methods. However, scientific literature lacks the ability to provide contributions that include MR technology education in the industrial design program. We, therefore, present an innovative laboratory with an integrated multidisciplinary approach that starts from the fundamentals of interaction design and aims to teach students how to design next-generation MR interfaces for the Metaverse. The lab combines theory and practice within three courses: Information Design, Information Systems, and Virtual Design and Simulation. Industrial design students follow a precise multidisciplinary method consisting of five steps, from state-of-the-art analysis to the presentation of a final group design of an MR user interface. Thus, we introduce a class case study by presenting the outcomes of a semester project in the field of household appliances. Evaluation of the teaching method is conducted through a semi-structured questionnaire. Preliminary results show positive outcomes from students in terms of acceptance, effectiveness, usefulness, efficiency, and satisfaction with the teaching method adopted for the laboratory.

The present paper focuses on the cultural value of making, identifying the methods for sharing the knowledge and the technical skills, particularly, in the context of digital design and fabrication. Starting from the renewed interest in manufacturing processes directly linked to creativity, sustainability, mass customization, practical innovation and experimentation, enhanced by the so called makers’ revolution and by the new arts and crafts movements, the paper explores the on going transformations in the manufacturing places focusing on the Fab Lab sites as new hubs for sharing knowledge and skills. The dissertation is supported by a practical experimentation, carried out by the authors, based on a co-design workshop and a proactive interdisciplinary activity addressed to identify the processes of social innovation involving both expert and not expert designers in the context of digital design and fabrication processes. As a result Bright Light, a small lamp, has been produced in order to finalize the co-design processes with a smart artefact.

Aim of this work is to present an innovative open Cyber Physical System (CPS) to monitor and control human exposure to ionizing radiation. Nowadays CPS and Internet of Things (IoT) are becoming ever more common in this field. This fact led to the implementation of systems composed of many computing nodes intercommunicating mutually and characterized by computational power being adequate to the task to be carried out. Each node has been designed to have an intelligent component making it able to handle some physical sensors and the interaction with the external world (communication with interface system, other sensors in the network, actuator to modify the environment conditions). The proposed system is able to monitor and control different radiation sources but in this work authors highlight the aspects concerning radon gas and its progeny.

Aim of this paper is to present an innovative personal dosimeter for ionizing radiations obtained using a multilevel approach based on smartphone, artificial intelligence (AI) and data fusion. Nowadays, ionizing radiations are gaining ever more attention by the scientific community due to their severe impact on human health. In particular, it is difficult to measure long lasting exposure to low doses of ionizing radiations and to assess their impact on human health. The proposed system measures the actual users’ exposition level by means of a data fusion technique of personal data (logs of their positions using smartphone’s GPS, flight hours, X-rays, etc.) and those sampled by public and open sensor networks measuring radon gas concentration. Furthermore, the system proposes a forecasting analysis of users’ annual exposition level using a binary neural network to identify "exposition risk profiles". In this way, the proposed system can be useful to influence users towards improving their lifestyles.