https://hdl.handle.net/10481/13706 2026-04-11T14:31:07Z 2026-04-11T14:31:07Z Vázquez Rodríguez, Víctor Valenzuela Segura, Elio Shepstone, Ricardo Megías Núñez, Carlos Miccichè, Gioacchino Ros Vidal, Eduardo Barranco Expósito, Francisco https://hdl.handle.net/10481/112339 2026-03-20T09:52:43Z

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.

Garach, Ignacio Hernandez, Nerea Herrera Maldonado, Luis Javier Ortuno, Francisco M. Rojas Ruiz, Ignacio https://hdl.handle.net/10481/111995 2026-03-09T13:18:03Z

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.

Sánchez Garrido, Jorge Jurado Caballero, Antonio Jiménez-López, Miguel Arnim, Balzer Prokoph, Heike Stephan, Maurice Berge, David Rodríguez Álvarez, Manuel Díaz Alonso, Antonio Javier https://hdl.handle.net/10481/111805 2026-03-02T12:26:23Z

A White Rabbit-Synchronized Accurate Time-Stamping Solution for the Small-Sized Cameras of the Cherenkov Telescope Array Sánchez Garrido, Jorge; Jurado Caballero, Antonio; Jiménez-López, Miguel; Arnim, Balzer; Prokoph, Heike; Stephan, Maurice; Berge, David; Rodríguez Álvarez, Manuel; Díaz Alonso, Antonio Javier This article presents the Zynq-embedded node for the Cherenkov telescope array (ZEN-CTA) node, a programmable system-on-chip (SoC) with White Rabbit (WR)-synchronization capability. It targets a solution for the uniform clock and trigger time-stamping module of the small-sized telescopes in the CTA. This module is tasked as a distributed acquisition device with a focus on obtaining time stamps for candidate Cherenkov events, which could be generated at potentially high rates from very-high-energy gamma rays and their subsequent distribution over Ethernet. In this context, the customized design of the ZEN-CTA node is examined thoroughly, including its generic implementation aspects and its main functional blocks. The design of the WR-assisted time-to-digital converters (TDCs) for time-stamping analog triggers is presented in detail alongside the implementation of an upgraded high-speed data path (1 Gb/s) for the WR-compatible Ethernet interfaces of the node. The new data path will feature a direct memory access engine for direct software transmissions and a hardware description language (HDL) coprocessor for high-speed forwarding. Next, the time-stamping accuracy of the WR-enhanced TDCs will be characterized alongside the forwarding efficiency of the new data path. Finally, conclusions are drawn, and the main contributions of this research are enumerated, a potential deployment within the CTA infrastructure to support the acquisition of Cherenkov light is considered, and additional use cases are mentioned. This work was supported in part by Amiga-6 Project Grant under Grant AYA2015-65973-C3-2-R and in part by Amiga-7 Project Grant under Grant RTI2018-096228-B-C3

Luque Sola, Niceto Rafael Garrido Alcázar, Jesús Alberto Carrillo Sánchez, Richard Rafael Coenen, Olivier J.-M. D. Ros Vidal, Eduardo https://hdl.handle.net/10481/111732 2026-03-01T09:36:20Z

Cerebellar Input Configuration Toward Object Model Abstraction in Manipulation Tasks Luque Sola, Niceto Rafael; Garrido Alcázar, Jesús Alberto; Carrillo Sánchez, Richard Rafael; Coenen, Olivier J.-M. D.; Ros Vidal, Eduardo It is widely assumed that the cerebellum is one of the main nervous centers involved in correcting and refining planned movement and accounting for disturbances occurring during movement, for instance, due to the manipulation of objects which affect the kinematics and dynamics of the robot-arm plant model. In this brief, we evaluate a way in which a cerebellar-like structure can store a model in the granular and molecular layers. Furthermore, we study how its microstructure and input representations (context labels and sensorimotor signals) can efficiently support model abstraction toward delivering accurate corrective torque values for increasing precision during different-object manipulation. We also describe how the explicit (object-related input labels) and implicit state input representations (sensorimotor signals) complement each other to better handle different models and allow interpolation between two already stored models. This facilitates accurate corrections during manipulations of new objects taking advantage of already stored models.

Luque Sola, Niceto Rafael Garrido Alcázar, Jesús Alberto Carrillo Sánchez, Richard Rafael Coenen, Olivier J.-M. D. Ros Vidal, Eduardo https://hdl.handle.net/10481/111731 2026-03-01T09:35:54Z

Cerebellarlike Corrective Model Inference Engine for Manipulation Tasks Luque Sola, Niceto Rafael; Garrido Alcázar, Jesús Alberto; Carrillo Sánchez, Richard Rafael; Coenen, Olivier J.-M. D.; Ros Vidal, Eduardo This paper presents how a simple cerebellumlike architecture can infer corrective models in the framework of a control task when manipulating objects that significantly affect the dynamics model of the system. The main motivation of this paper is to evaluate a simplified bio-mimetic approach in the framework of a manipulation task. More concretely, the paper focuses on how the model inference process takes place within a feedforward control loop based on the cerebellar structure and on how these internal models are built up by means of biologically plausible synaptic adaptation mechanisms. This kind of investigation may provide clues on how biology achieves accurate control of non-stiff-joint robot with low-power actuators which involve controlling systems with high inertial components. This paper studies how a basic temporal-correlation kernel including long-term depression (LTD) and a constant long-term potentiation (LTP) at parallel fiber-Purkinje cell synapses can effectively infer corrective models. We evaluate how this spike-timing-dependent plasticity correlates sensorimotor activity arriving through the parallel fibers with teaching signals (dependent on error estimates) arriving through the climbing fibers from the inferior olive. This paper addresses the study of how these LTD and LTP components need to be well balanced with each other to achieve accurate learning. This is of interest to evaluate the relevant role of homeostatic mechanisms in biological systems where adaptation occurs in a distributed manner. Furthermore, we illustrate how the temporal-correlation kernel can also work in the presence of transmission delays in sensorimotor pathways. We use a cerebellumlike spiking neural network which stores the corrective models as well-structured weight patterns distributed among the parallel fibers to Purkinje cell connections.