Annalisa Liccardo

The increasing penetration of inverter-based resources and the growing complexity of modern power systems have made voltage stability assessment a critical aspect of transmission network operation. This paper presents a review of the main methods and indices adopted for voltage stability assessment, with emphasis on their applicability to real-time monitoring and inverter-dominated grids. The review covers both long-term and short-term voltage stability approaches, including L-index methods, Jacobian- and singular-value-based techniques, Lyapunov-exponent-based methods, trajectory-based indices, and recent artificial intelligence applications. For each method, the main advantages, limitations, computational requirements, and suitability for PMU-based monitoring are discussed. The analysis shows that no single indicator can fully describe all voltage instability phenomena. Instead, combining complementary static and dynamic indices provides a more complete basis for wide-area monitoring and real-time operational support in future power systems. Moreover, the main open challenges related to threshold definition, robustness under evolving grid conditions, and the integration of data-driven techniques into operational stability assessment schemes are discussed.

This study presents a measurement framework for the evaluation and online quality estimation of the Wire Arc Additive Manufacturing process based on Cold Metal Transfer (WAAM–CMT). Although several monitoring approaches have been proposed for WAAM, the identification of reliable non-invasive indicators capable of capturing process stability in real time remains challenging due to the highly non-stationary nature of arc current signals. A non-invasive monitoring strategy based on electrical current acquisition was implemented, using a clamp-type sensor to acquire the electrical current signal during the welding process. The acquired current signals were analyzed in both the time and frequency domains. To address the inherent variability of the current waveform, a wavelet coherence (WC) approach was adopted, enabling localized time–frequency correlation analysis between the measured current and a reference waveform. From this representation, a Mean Global Wavelet Coherence (MGWC) index was derived as a quantitative indicator of process stability. The proposed methodology showed effectiveness in distinguishing between stable and unstable deposition regimes, linking signal irregularities to the geometric quality of the deposited tracks. In addition, measurement uncertainty was explicitly evaluated by combining probability density functions derived from instrument specifications with Monte Carlo propagation, enabling the estimation of the confidence level associated with the monitoring indicator. The resulting framework integrates electrical current sensing, time–frequency signal analysis, and uncertainty quantification, providing a scalable and non-invasive solution for real-time monitoring and quality assessment in WAAM–CMT processes.

Accurate and automatic extraction of the udder skin surface temperature (USST) from thermal imagery enables continuous, non-invasive monitoring of udder health. The literature indicates that USST correlates with somatic cell count (SCC), the primary marker of mastitis activity, provided that udder regions are precisely segmented. Building on our previous system deployed on-farm, which integrated thermal imaging with robotic milking for real-time monitoring, we focus this paper on the segmentation component to obtain a precise yet lightweight, farm-deployable model. We explore compact binary segmentation models that pair the newly released DINOv3-initialised encoders with lightweight decoders: ConvNeXt + Feature Pyramid Network (FPN) and ViT + Dense Prediction Transformer (DPT). Our training follows four stages: (i) train the decoder while keeping the encoder frozen; (ii) apply isomorphic pruning to remove redundant, shape-consistent channels and feature dimensions while preserving tensor interfaces; (iii) unfreeze the pruned encoder and fine-tune the whole network to recover performance; and (iv) apply post-training quantisation to produce FP16 variants. We evaluate encoders across scales and report test intersection over union (IoU), parameter count, and multiply–accumulate operations (MACs), selecting via the IoU-cost frontier at full precision. While a non-pruned ConvNeXt_tiny backbone attains the highest IoU, a pruned ConvNeXt_small achieves comparable IoU at a lower cost and is therefore our Pareto-optimal choice for edge deployment. Such a model (ConvNeXt_small+FPN) achieves an 81.68% IoU with 25.44M parameters and 32.60B MACs. It is exported to ONNX and quantised, and deployed over an Nvidia Jetson Orin Nano as a practical, offline, and privacy-preserving edge solution for farms. The deployed FP16-quantised ONNX model with CUDA-accelerated runtime achieves an average latency of 81.6 ms per frame at the 15W power profile on the Jetson Orin Nano. The dataset, source code, training logs, model weights, and Jetson-ready Docker image used for this study are released open-source to ensure full reproducibility and a fair comparison.

This paper presents the Instrumentation and Measurements Metaverse Laboratory (IM-MetaLAB), an immersive virtual laboratory developed within the metaverse to support hands-on training in instrumentation and measurement. Unlike previous Augmented Reality (AR)/Virtual Reality (VR)-based solutions or simulated environments, IM-MetaLAB integrates real laboratory instruments through digital twins that communicate in real time with their physical counterparts using Standard Commands for Programmable Instruments (SCPI) commands over Message Queuing Telemetry Transport (MQTT). The platform supports synchronous and asynchronous learning modes, promotes collaborative activities among students, and replicates real-world laboratory procedures in a safe and accessible 3D environment. A representative case study, focused on root means square (RMS) voltage measurement, demonstrates the system’s capabilities in executing full experimental workflows. Comparative analysis with traditional, online, and remote lab-based teaching methods was conducted using key performance indicators such as student engagement, completion rate, and examination scores. The results indicate that IM-MetaLAB significantly improves learning outcomes and practical proficiency. Ongoing work addresses scalability and cost-reduction through the implementation of a custom hardware interface.

Mastitis is a significant challenge in the buffalo industry, affecting both milk production and animal health and resulting in economic losses. This study presents the first fully automated AI-driven thermal imaging system integrated with robotic milking, specifically developed for the real-time, non-invasive monitoring of udder health in Italian Mediterranean buffalo. Unlike traditional approaches, the system leverages the synchronized acquisition of thermal images during milking and compensates for environmental variables through a calibrated weather station. A transformer-based neural network (SegFormer) segments the udder area, enabling the extraction of maximum udder skin surface temperature (USST), which is significantly correlated with somatic cell count (SCC). Initial trials demonstrate the feasibility of this approach in operational farm environments, paving the way for scalable, precision diagnostics of subclinical mastitis. This work represents a critical step toward intelligent, automated systems for early detection and intervention, improving animal welfare and reducing antibiotic use.

This paper presents an MQTT-based interface for remote control of GPIB-compliant instrumentation, designed to ensure that its integration does not degrade the user experience in remote laboratories. The system consists of an ESP32-based hardware interface with a GPIB connector, a Wi-Fi module, and an electronic control unit to manage communication with remote clients. The software architecture uses the lightweight MQTT protocol for data transfer, enabling real-time access to measurements and remote commands without significant latency or added complexity. A key focus is to assess whether the interface affects the usability and responsiveness of remote sessions. The system translates IEEE 488 commands into MQTT messages, enabling seamless integration with IoT infrastructures and supporting both read and write operations. To ensure reliability, the firmware includes a watchdog timer to prevent system hangs and autonomously manages network connectivity. The aim is to demonstrate that the proposed solution can improve remote control efficiency without compromising user experience intuitiveness or performance.