https://hdl.handle.net/10481/13705 Thu, 16 Apr 2026 07:55:47 GMT 2026-04-16T07:55:47Z https://hdl.handle.net/10481/112339 Remote handling operation for IFMIF-DONES supported by time-sensitive networking Vázquez Rodríguez, Víctor; Valenzuela Segura, Elio; Shepstone, Ricardo; Megías Núñez, Carlos; Miccichè, Gioacchino; Ros Vidal, Eduardo; Barranco Expósito, Francisco Experimental fusion research facilities, such as the International Fusion Materials Irradiation Facility-DEMO Oriented Neutron Source (IFMIF-DONES), require advanced remote handling (RH) systems to perform maintenance and inspection tasks in a safe and reliable manner, due to their intrinsic high-radiation nature. The mixed-criticality requirements of the data streams used in these systems force the deployment of separate networks and communication technologies. Commonly, it includes fieldbuses for traffic control, standard Ethernet for video and general-purpose traffic, and dedicated networks for the most critical safety-related signals. This fragmentation leads to complex and costly deployments and also prevents the application of models for predictive maintenance or advanced monitoring. The time-sensitive networking (TSN) technology stack aims to provide deterministic behaviour for data transmission over standard Ethernet, allowing for convergence on a single network and ensuring bounded latencies for critical traffic. In this work, we propose a design and validate the TSN-based communication architecture for the RH system of IFMIF-DONES. The design ensures bounded delivery times for safety-critical interlock signals, achieving a worst-case delay under 30 us even under high network load. The proposed network is also validated in a real robotic teleoperation task, where artificial intelligence is applied for object detection and tracking, using mixed-criticality video streams. Our results show that TSN traffic shapers are essential in providing the necessary latency and bandwidth guarantees for such teleoperation tasks, enabling network convergence in this kind of deployments. https://hdl.handle.net/10481/112339 https://hdl.handle.net/10481/112114 Assessing Bias in the Evaluation of Blood Glucose Prediction Models Rodríguez León, Ciro; Avilés-Pérez, María Dolores; Baños Legrán, Oresti; Lopez-Ibarra Lozano, Pablo J.; Muñoz Torres, Manuel Eduardo; Quesada Charneco, Miguel; Villalonga Palliser, Claudia Diabetes mellitus (DM) poses a critical global health challenge, with type 1 diabetes (T1D) patients presenting unique difficulties in maintaining a safe blood glucose level (BGL). This work demonstrates that evaluating BGL prediction models without considering different BGL ranges, hypoglycemia, hyperglycemia, and normoglycemia, introduces bias in assessing the prediction results. Data are obtained from the T1DiabetesGranada dataset, comprising over 22.5 million measured BGL values recorded at 15-min intervals, and are preprocessed into a uniform format for supervised learning. Time series are segmented into windows with a 2-h history length and prediction horizons of 30 and 60 min. An LSTM architecture is used to predict BGL values due to its ability to capture temporal dependencies. The evaluation combines traditional non-clinical metrics (RMSE, MAE, MAPE) with clinical metrics derived from the Clarke Error Grid. The newly proposed evaluation strategy assesses BGL prediction models performance not only across the entire BGL range but also within different BGL ranges. Results indicate that evaluation metrics computed using the entire BGL range may suggest satisfactory BGL prediction model performance. However, significant deficiencies emerge in hypoglycemic ranges, implying that conventional evaluation strategies may overestimate BGL prediction models capabilities. These findings highlight the need for a comprehensive evaluation strategy in different BGL ranges to avoid bias, especially while evaluating clinically critical regions. https://hdl.handle.net/10481/112114 https://hdl.handle.net/10481/112037 Clustering type 1 diabetes patients based on blood glucose level measurements; Clustering type 1 diabetes patients based on blood glucose level measurements Rodríguez León, Ciro; Avilés-Pérez, M. D.; Baños, Oresti; Lopez-Ibarra Lozano, Pablo J.; Muñoz Torres, Manuel Eduardo; Quesada Charneco, Miguel; Villalonga Palliser, Claudia Improving diabetes mellitus management requires accurate blood glucose predictions to prevent dangerous glycemic extreme events; however, traditional models often struggle with the inherent fluctuations in blood glucose levels. In this work, the identification of distinct patient groups exhibiting common characteristics based on longitudinal continuous glucose monitoring (CGM) data is explored. This clustering approach aims to offer valuable insights for both enhancing predictive modeling accuracy and informing clinical relevance. Raw CGM data was transformed into structured feature sets using statistical descriptors calculated across clinically meaningful time intervals. Following outlier removal, multiple clustering algorithms were evaluated, with the optimal solution selected based on internal metrics and validation by clinical experts. The analysis revealed six distinct patient groups, each characterized by unique glycemic behaviors, including well-controlled, prone to hyperglycemia, and prone to hypoglycemia profiles. It is proposed that these identified clusters can improve glucose prediction by strategically balancing personalized and generalized approaches. Furthermore, valuable insights into individual metabolic variability can be obtained, potentially supporting the development of tailored treatment strategies. While acknowledging limitations inherent in data transformation and expert-driven evaluation, this clustering methodology represents a significant step towards more precise and data-driven diabetes mellitus management. https://hdl.handle.net/10481/112037 https://hdl.handle.net/10481/112035 Exploring the Influence of Patients’ Heterogeneity on Automated Prediction of Blood Glucose Levels in Type 1 Diabetes Baños Legrán, Oresti; Avilés-Pérez, María Dolores; Gayaroa, Álvaro; Lopez-Ibarra Lozano, Pablo J.; Muñoz Torres, Manuel Eduardo; Quesada Charneco, Miguel; Rodríguez León, Ciro; Villalonga Palliser, Claudia This work investigates the impact of patients’ heterogeneity, including factors such as gender, age, and clinical conditions, on the performance of machine learning models in predicting blood glucose levels in individuals with Type 1 Diabetes. Using the T1DiabetesGranada dataset, various datasets were generated based on these heterogeneity factors. Three popular prediction models —a Linear model, an LSTM neural network, and a CNN— were applied to both the generated datasets and the complete dataset to measure prediction performance at a 30-minute prediction horizon. The preliminary results suggest that incorporating patient-specific heterogeneity factors generally improves prediction performance, highlighting the existence of bias in standard blood glucose level prediction models. Future research should explore whether these findings hold in other related datasets. This work has been supported by the PID2023-148188OA-I00 project ”RELIEF-T1D” which is funded by MICIU/AEI/10.13039/501100011033 and ERDF EU. https://hdl.handle.net/10481/112035 https://hdl.handle.net/10481/111995 Integrated Transcriptomic and Histological Analysis of TP53/CTNNB1 Mutations and Microvascular Invasion in Hepatocellular Carcinoma Garach, Ignacio; Hernandez, Nerea; Herrera Maldonado, Luis Javier; Ortuno, Francisco M.; Rojas Ruiz, Ignacio Background/Objectives: Hepatocellular carcinoma (HCC) shows marked molecular and histopathological heterogeneity. Among the alterations most strongly associated with clinical outcome are mutations in TP53 and CTNNB1, as well as the presence of microvascular invasion (MVI). Although these factors are well established as prognostic indicators, how their molecular effects relate to tumor morphology remains unclear. In this work, we studied transcriptomic changes linked to TP53 and CTNNB1 mutational status and to MVI, and examined whether these changes are reflected in routine histology. Methods: RNA sequencing data from HCC samples annotated for mutations and vascular invasion were analyzed using differential expression analysis combined with machine learning-based feature selection to characterize the underlying transcriptional programs. In parallel, we trained a weakly supervised multitask deep learning model on hematoxylin and eosin-stained whole-slide images using slide-level labels only, without spatial annotations, to assess whether these features could be inferred from global histological patterns. Results: Distinct gene expression profiles were observed for TP53-mutated, CTNNB1-mutated, and MVI-positive tumors, involving pathways related to proliferation, metabolism, and invasion. Image-based models were able to capture morphological patterns associated with these states, achieving above-random discrimination with variable performance across tasks. Conclusions: Taken together, these results support the existence of coherent biological programs underlying key risk determinants in HCC and indicate that their phenotypic effects are, at least in part, detectable in routine histopathology. This provides a rationale for integrative morpho-molecular approaches to risk assessment in HCC. https://hdl.handle.net/10481/111995