Food choices are complex functions of several elements that could change over time. Nowadays consumers appear careful about sustainable food consumption: the behavior of “food citizenship”, as the practice to support a sustainable food system during the consumption actions, arises. This study aims to recognize the existence of food choice behaviors in the contemporary scenario and to investigate the relation between the food choice factors and the behaviors recognized. Following a quantitative research method, a sample of 380 participants, recruited from a traditional Italian food and wine event, completed a questionnaire in order to detect their attitude about food. Four current food choice behaviors were recognized: The Individualist, The Foodie, The Environmentalist and The Health enthusiast. The relation between food choice factors and food choice behaviors was explained. Several stakeholders could benefit from the study results, in order to better understand how to adapt products and marketing strategies to satisfy the emerging customer’s needs and awareness. Even if a person can identify themselves within a single food choice behavior, they become aware of other choice models expanding their personal point of view. Finally, new research scenarios arose for the researchers.

Early warning systems (EWSs) are crucial for optimising predictive maintenance strategies, especially in the industrial sector, where machine failures often cause significant downtime and economic losses. This research details the creation and evaluation of an EWS that incorporates deep learning methods, particularly using Long Short-Term Memory (LSTM) networks enhanced with attention layers to predict critical machine faults. The proposed system is designed to process time-series data collected from an industrial printing machine’s embosser component, identifying error patterns that could lead to operational disruptions. The dataset was preprocessed through feature selection, normalisation, and time-series transformation. A multi-model classification strategy was adopted, with each LSTM-based model trained to detect a specific class of frequent errors. Experimental results show that the system can predict failure events up to 10 time units in advance, with the best-performing model achieving an AUROC of 0.93 and recall above 90%. Results indicate that the proposed approach successfully predicts failure events, demonstrating the potential of EWSs powered by deep learning for enhancing predictive maintenance strategies. By integrating artificial intelligence with real-time monitoring, this study highlights how intelligent EWSs can improve industrial efficiency, reduce unplanned downtime, and optimise maintenance operations.

The adoption of the Internet of Things (IoT) technology in the Industry 4.0 framework is increasingly on the rise. Industrial organizations, including those operating in the aerospace sector, are interested in exploiting sensed data in a virtual environment. The Digital Twin (DT) emerges as the virtual counterpart of a real-world system for analysing, monitoring and simulating its behaviour and supporting decisions process. While the concepts of both IoT and DT are widely discussed by the academia, empirical demonstrations on their synergic integration seem to lack in the literature. Considering the HexaSFDT framework, this paper aims to narrow this gap and to discuss a practical case study of an aerospace company. Moreover, it focuses on contextualizing the framework in a process-oriented DT. Following a collaborative approach, several interviews have been conducted with both operational and strategic profiles of the aerospace company. Data flows sensed from the industrial scenario have been analysed to monitor the behaviour of the critical materials management process. Accordingly, the HexaSFDT framework has been customized for fitting the process of the case study. The paper provides a double implication. It discusses the adoption of IoT and DT technologies by presenting an empirical research. Also, through an industrial case study, it supports practitioners, and in particular aerospace companies, in understanding how these technologies can be leveraged to assist people in decision-making.

An important aspect of health monitoring is effective knowledge of food consumed. In this regard, a methodology of analysis is proposed in order to know in advance the food choices of the final consumers. This can give added value to the agricultural productions of the territory and of the farmers, facilitating the direct relationship between agriculture and the final consumer. To build a prediction model, web monitoring and traditional marketing analysis will be performed. The results of analysis can be used to sensitize the consumers towards a greater alimentary awareness, helping to manage of diet-related diseases like obesity, diabetes, and even cardiovascular diseases.