In the paper, the problem of the effects of people motion between two separated population under epidemic spread is considered, referring in particular the COVID-19 case. Model, analysis and design of an optimal switching control emulating the different steps of containment intervention strategies are presented. In particular, the case of an asymmetric people flow is studied, considering one small population receiving a people flow from a second one, more numerous. While the analysis is carried on in the general case, the hypothesis that the small outgoing flux of individuals from the largest population does not change its steady state conditions. After equilibrium and stability analysis of the proposed mathematical model, the philosophy of the switching control is discussed, the control is designed and the results of numerical simulations are reported to support the effectiveness of the proposed approach.
We introduce an efficient, scalable, and ready-to-use approach for generating knowledge graphs (KGs) based on any given ontology. Our method leverages large language model (LLM)-based retrieval-augmented generation (RAG) as a source of high-quality text data. It employs two agents: the first extracts entities and triples from the text corpus maintained by the RAG, while the second links similar entities by applying LLM reasoning grounded in pragmatics. This approach requires no fine-tuning or additional AI training, relying solely on off-the-shelf technologies. Furthermore, its use of RAG enables it to handle a text corpus of arbitrary size. We evaluated our method in the context of high-pressure die-casting, focusing on defect classification. In the absence of annotated datasets, a manual evaluation of the resulting KG and confusion matrix revealed a threat score of approximately 85
This paper presents a model-based control system design for a novel tethered unmanned aerial vehicle (UAV)-unmanned ground vehicle (UGV) system developed in collaboration with Aurrigo, aligning with their autonomous baggage tug, the Auto-DollyTug®. The UAV provides overhead surveillance while tracking the ground vehicle, with power and data transmission managed via a custom-designed tether system. A comprehensive dynamic model of the tethered quadcopter is developed, incorporating aerodynamic drag, environmental disturbances, and tether forces. Simulation studies of a dual-loop control architecture design are presented. The simulated scenarios assess system performance under varying UAV speeds, flight radius, and wind conditions, and make use of integral absolute error (IAE) metrics. Results highlight that UAV velocities (≈2.9 m/s) yield satisfactory tracking, while higher wind speeds and larger radius reduce performance. Preliminary studies involving a fuzzy logic-based tether control strategy are also introduced, with surface plots generated to guide adaptive tether length adjustment.
This study investigates the statistical distribution of inter-event times for lava fountains at Mt. Etna (2011-2022), demonstrating a power-law scaling indicative of Self-Organized Criticality (SOC). Furthermore, a modified version of the Bak-Tang-Wiesenfeld (BTW) model, referred to as the Volcano Sandpile (VS) model, tailored to simulate these inter-event times is investigated. While this preliminary model provides initial insights into the dynamics of lava fountain recurrence, further refinements are necessary to improve its accuracy in capturing the observed variability in eruption inter-event times and its applicability to more complex volcanic systems.
Solving the Stockyard Planning Problem (SPP) is a critical task in the daily business of transhipment points such as harbors where bulk material is freighted. In our previous work presented at the 21st International Conference on Informatics in Control, Automation and Robotics (ICINCO) 2024 [5] we presented a method to solve the SPP in a realistic scenario of a dry bulk port using a hybrid approach of constraint programming and greedy search algorithms. This paper proposes an extension to this method with an optimized score function and refined constraints. With this refined approach it is possible to solve more complex scenarios and the improved score function enables a more accurate representation of the reality. Additionally, we extended our test setting to validate the effectiveness of the new model.
