Supporting consumers in making autonomous food choices that are sustainable and nutritionally complete is an increasingly complex task, that must take into account several needs to foster eating habits of health-conscious consumers, while reducing food waste and environmental impact. While generative AI and Large Language Models (LLMs) show promising results in this domain due to their natural language processing capabilities, they suffer from critical limitations, including hallucinations, knowledge gaps, and a lack of handling factual information. To mitigate such limitations, Retrieval Augmented Generation (RAG), which retrieves relevant information from external sources to enhance the capabilities of LLMs, has shown effectiveness in many domains. However, existing RAG approaches typically operate on unstructured text that lacks sophisticated symbolic representations of complex domain knowledge. This work proposes an ontology-enhanced conversational food advisory system, that integrates a modular ontology, named FoCOSA (Food Consumer-Oriented Sustainability-Aware), within several key tasks of a RAG-based system, enhancing LLM reasoning with domain knowledge, while the LLM enhances the interpretation of user requests, thus improving retrieval effectiveness and interaction fluidity. Experimental evaluations demonstrate the efficacy of the approach, and the study concludes with guidelines for selecting appropriate settings for food recommendation scenarios considering the complexity of natural language queries and other contextual factors.

Abstract In recent years, Cyber Physical Production Systems and Digital Threads opened the vision on the importance of data modelling and management to lead the smart factory towards a full-fledged vertical and horizontal integration. Vertical integration refers to the full connection of smart factory levels from the work centers on the shop floor up to the business layer. Horizontal integration is realised when a single smart factory participates in multiple interleaved supply chains with different roles (e.g., main producer, supplier), sharing data and services and forming a Cyber Physical Production Network. In such an interconnected world, data and services become fundamental elements in the cyberspace to implement advanced data-driven applications such as production scheduling, energy consumption optimisation, anomaly detection, predictive maintenance, change management in Product Lifecycle Management, process monitoring and so forth. In this paper, we propose a methodology that guides the design of a portfolio of data-oriented services in a Cyber Physical Production Network. The methodology starts from the goals of the actors in the network, as well as their requirements on data and functions. Therefore, a data model is designed to represent the information shared across actors according to three interleaved perspectives, namely, product, process and industrial assets. Finally, multi-perspective data-oriented services for collecting, monitoring, dispatching and displaying data are built on top of the data model, according to the three perspectives. The methodology also includes a set of access policies for the actors in order to enable controlled access to data and services. The methodology is tested on a real case study for the production of valves in deep and ultra-deep water applications. Experimental validation in the real case study demonstrates the benefits of providing a methodological support for the design of multi-perspective data-oriented services in Cyber Physical Production Networks, both in terms of usability of the data navigation through the services and in terms of service performances in presence of Big Data.

Abstract Cyber-physical systems are hybrid networked cyber and engineered physical elements that record data (e.g. using sensors), analyse them using connected services, influence physical processes and interact with human actors using multi-channel interfaces. Examples of CPS interacting with humans in industrial production environments are the so-called cyber-physical production systems (CPPS), where operators supervise the industrial machines, according to the human-in-the-loop paradigm. In this scenario, research challenges for implementing CPPS resilience, promptly reacting to faults, concern: (i) the complex structure of CPPS, which cannot be addressed as a monolithic system, but as a dynamic ecosystem of single CPS interacting and influencing each other; (ii) the volume, velocity and variety of data (Big Data) on which resilience is based, which call for novel methods and techniques to ensure recovery procedures; (iii) the involvement of human factors in these systems. In this paper, we address the design of resilient cyber-physical production systems (R-CPPS) in digital factories by facing these challenges. Specifically, each component of the R-CPPS is modelled as a smart machine, that is, a cyber-physical system equipped with a set of recovery services, a Sensor Data API used to collect sensor data acquired from the physical side for monitoring the component behaviour, and an operator interface for displaying detected anomalous conditions and notifying necessary recovery actions to on-field operators. A context-based mediator, at shop floor level, is in charge of ensuring resilience by gathering data from the CPPS, selecting the proper recovery actions and invoking corresponding recovery services on the target CPS. Finally, data summarisation and relevance evaluation techniques are used for supporting the identification of anomalous conditions in the presence of high volume and velocity of data collected through the Sensor Data API. The approach is validated in a food industry real case study.

Digital transformation and the adoption of ICT technologies in the factory of the future are growing faster and faster. In particular, data exploration methods and techniques are enabling the development of data-intensive Remote Monitoring Services for anomaly detection and predictive maintenance purposes. Remote Monitoring Services involve different actors across organizations. The Original Equipment Manufacturer explores high volume of data collected by sensors on the monitored machines to provide anomaly detection and predictive maintenance services. Insurance agencies may provide support to sustain maintenance costs. Spare parts suppliers can schedule the delivery of mechanical parts required for maintenance interventions. In this scenario, trust among participants becomes a critical issue. On the one hand, providers of anomaly detection and predictive maintenance services as well as insurance agencies must trust the way machines have been used by collecting and analysing sensors data. On the other hand, owners of monitored machines must trust the use of collected data to implement services, based on which maintenance costs are calculated. The goal of this paper is to leverage blockchain and smart contracts to ensure the required level of trust when implementing data exploration for Remote Monitoring Services. Events occurring on the monitored machines are stored as transactions in a blockchain-based system, to ensure non repudiation. Moreover, trust-demanding services are implemented as smart contracts, to guarantee the required level of trustworthiness among participants. The approach is integrated with a tool for data exploration in the digital factory, and has been validated taking into account performances and cost requirements.