The increasing frequency and severity of forest fires necessitate early detection and rapid response to mitigate their impact. This project aims to design a cyber-physical system for early detection and rapid response to forest fires using advanced technologies. The system incorporates Internet of Things sensors and autonomous unmanned aerial and ground vehicles controlled by the robot operating system. An IoT-based wildfire detection node continuously monitors environmental conditions, enabling early fire detection. Upon fire detection, a UAV autonomously surveys the area to precisely locate the fire and can deploy an extinguishing payload or provide data for decision-making. The UAV communicates the fire’s precise location to a collaborative UGV, which autonomously reaches the designated area to support ground-based firefighters. The CPS includes a ground control station with web-based dashboards for real-time monitoring of system parameters and telemetry data from UAVs and UGVs. The article demonstrates the real-time fire detection capabilities of the proposed system using simulated forest fire scenarios. The objective is to provide a practical approach using open-source technologies for early detection and extinguishing of forest fires, with potential applications in various industries, surveillance, and precision agriculture.

The demand for a wide choice of food that is safe and palatable increases every day. Consumers do not accept off-flavors that have atypical odors resulting from internal deterioration or contamination by substances alien to the food. Odor response depends on the volatile organic compounds (VOCs), and their detection can provide information about food quality. Gas chromatography/mass spectrometry is the most powerful method available for the detection of VOC. However, it is laborious, costly, and requires the presence of a trained operator. To develop a faster analytic tool, we designed a non-Faradaic impedimetric biosensor for monitoring the presence of VOCs involved in food spoilage. The biosensor is based on the use of the pig odorant-binding protein (pOBP) as the molecular recognition element. We evaluated the affinity of pOBP for three different volatile organic compounds (1-octen-3-ol, trans-2-hexen-1-ol, and hexanal) related to food spoilage. We developed an electrochemical biosensor conducting impedimetric measurements in liquid and air samples. The impedance changes allowed us to detect each VOC sample at a minimum concentration of 0.1 μM.

In this paper, we show that the evolution of artificial intelligence (AI) and its increased presence within an interactive system pushes designers to rethink the way in which AI and its users interact and to highlight users’ feelings towards AI. For novice designers, it is crucial to acknowledge that both the user and artificial intelligence possess decision-making capabilities. Such a process may involve mediation between humans and artificial intelligence. This process should also consider the mutual learning that can occur between the two entities over time. Therefore, we explain how to adapt the Human-Centered Design (HCD) process to give centrality to AI as the user, further empowering the interactive system, and to adapt the interaction design to the actual capabilities, limitations, and potentialities of AI. This is to encourage designers to explore the interactions between AI and humans and focus on the potential user experience. We achieve such centrality by extracting and formalizing a new category of AI requirements. We have provocatively named this extension: “Intelligence-Centered”. A design workshop with MsC HCI students was carried out as a case study supporting this change of perspective in design.

In this work, we explore the role of augmented reality as a meta-user interface, with particular reference to its applications for interactive fitting room systems and the impact on the related shopping experience. Starting from literature and existing systems, we synthesized a set of nine interaction design patterns to develop AR fitting rooms and to support the shopping experience. The patterns were evaluated through a focus group with possible stakeholders with the aim of evaluating and envisioning the effects on the shopping experience. The focus group analysis shows as a result that the shopping experience related to an AR fitting room based on the proposed patterns is influenced by three main factors, namely: the perception of the utility, the ability to generate interest and curiosity, and the perceived comfort of the interaction and environment in which the system is installed. As a further result, the study shows that the patterns can successfully support these factors, but some elements that emerged from the focus group should be more investigated and taken into consideration by the designers.

The European Agency for Safety and Health at Work considers Smart Personal Protective Equipment as “Intelligent Protection For The Future”. It mainly consists of electronic components that collect data about their use, the workers who wear them, and the working environment. This paper proposes a distributed solution of Smart Personal Protective Equipment for the safety monitoring of Lone Workers by adopting low-cost electronic devices. In addition to the same hazards as anyone else, Lone Workers need additional and specific systems due to the higher risk they run on a work site. To this end, the Edge-Computing paradigm can be adopted to deploy an architecture embedding wearable devices, which alerts safety managers when workers do not wear the prescribed Personal Protective Equipment and supports a fast rescue when a worker seeks help or an accidental fall is automatically detected. The proposed system is a work-in-progress which provides an architecture design to accommodate different requirements, namely the deployment difficulties at temporary and large working sites, the maintenance and connectivity recurring cost issues, the respect for the workers’ privacy, and the simplicity of use for workers and their supervisors.