Probabilistic electricity price forecasting (PEPF) is a highly complex task with broad economic and operational impact. Recent advances in time series foundation models (TSFMs) offer promising tools to improve PEPF performance. In contrast, PEPF provides a challenging platform for evaluating and accelerating the development of general TSFMs. Despite their potential synergies, TSFMs have received limited attention in the PEPF literature, while the PEPF task remains largely unexplored in the TSFM context. This work aims to bridge these currently parallel research streams, fostering convergence and cross-fertilization to advance both fields. Focusing on Moirai, an open-source probabilistic framework with native covariate support and fine-tuning capabilities, we set up a comprehensive benchmark against specialized neural network-based PEPF methods across multiple market regions characterized by high variability and heterogeneous conditions. Additionally, we systematically explore fine-tuning strategies and model configurations, including context lengths and exogenous variable usage, to assess their impact on probabilistic forecasting accuracy. Experimental results indicate that Moirai provides promising zero-shot predictions, though it still underperforms compared to domain-specific neural networks. Fine-tuning improves calibration, while the architecture does not yet fully leverage exogenous features. Taken together, these observations offer valuable insights to foster future developments. We release our code through an open repository to facilitate collaborative progress within the PEPF and TSFM communities.

Deep learning (DL) models have shown promise for skeletal muscle (SM) segmentation in MR images, which is crucial for extracting biomarkers in neuromuscular disorders (NMDs). However, to ensure safe clinical use, models should provide uncertainty estimates, allowing radiologists to assess predictions and intervene when needed. Foundation Models (FMs) have the potential to play a significant role due to their strong generalization capabilities and well-calibrated predictions. However, their applicability in this context has not yet been explored. This study aims to develop an accurate and trustworthy technique by fine-tuning FMs to delineate fatty-infiltrated SM fascicles in NMD patients. We fine-tuned Segment Anything Model (SAM) and MedSAM using two configurations - encoder/decoder and decoder only - and compared their performance against state-of-the-art 2D and 3D nnU-Net using a dataset of thigh MR images from 76 NMD patients, categorized into Early, Moderate, and Severe fatty infiltration groups. Accuracy was evaluated using the Dice Similarity Coefficient (DSC), while Uncertainty Quantification (UQ) was evaluated using the Expected Calibration Error (ECE) and the Negative Log-Likelihood (NLL). Deep Ensembles were used to convey epistemic uncertainty in addition to the aleatoric counterpart. SAM's fine-tuned encoder/decoder outperformed nnU-Net 3D in Moderate and Severe cases (DSC: 0.886 vs 0.883 and 0.857 vs 0.850) and was comparable in Early (DSC: 0.925). MedSAM did not show an advantage over SAM. Regarding UQ, SAM exhibited superior calibration in Moderate and Severe groups (ECE: 3.6% vs. 5.1% and 3.3% vs. 7.1%), CONCLUSIONS:: In conclusion, our findings demonstrate that fine-tuning SAM yields superior performance, considering both accuracy and UQ metrics, highlighting its enhanced reliability in challenging NMD imaging scenarios.

Flexibility and reconfigurability represent crucial challenges to be addressed to achieve sustainable remanufacturing processes, supporting efficient End-Of-Life treatment of heterogeneous mixes of post-use products. In this context, Printed Circuit Boards (PCB) represent a key target to be addressed. In this work, we propose an integrated automation system fostering the implementation of flexible PCB remanufacturing plants. In particular, we extend previous works by introducing on-line scheduling facilities. To this end, we developed a predictive-reactive algorithm by a Unified Particle Swarm Optimization form. The objective function combines the total weighted tardiness and the idle time of the rework machine to minimize energy consumption due to dissipations. We show the effectiveness of the proposed approach by application to the STIIMA-CNR De/Re-Manufacturing pilot plant, reporting improved performances compared to previously implemented scheduling rules.