Doctoral School

Reading date: 25/02/2026

• PRATS BISBE, ALBA: Nous entorns interactius de realitat virtual immersiva aplicats a la neurorehabilitació de funcions cognitives i sensoriomotores
  
  Author: PRATS BISBE, ALBA
  Thesis file: (contact the Doctoral School to confirm you have a valid doctoral degree and to get the link to the thesis)
  Programme: DOCTORAL DEGREE IN BIOMEDICAL ENGINEERING
  Department: Department of Automatic Control (ESAII)
  Mode: Normal
  Deposit date: 29/01/2026
  Reading date: pending
  Reading time: pending
  Reading place: pending
  Thesis director: JANE CAMPOS, RAIMON | OPISSO SALLERAS, ELOY
  Thesis abstract: Recovery following an acquired brain injury (ABI) represents a major challenge with significant implications for health, quality of life, and socioeconomic burden. Advances in medicine and neurorehabilitation —particularly personalized, patient-centred clinical practice— have contributed to improved outcomes by promoting autonomy and participation in society. Technology has become a cornerstone in this continuous improvement, and virtual reality (VR) has emerged as a promising therapeutic tool. VR immersive properties, ergonomic interaction capabilities, ecological validity, and intrinsic advantages of digitalisation can enhance motor learning and cognitive improvement. However, there is still no conclusive evidence regarding VR clinically significant effects and consistent adoption in routine hospital practice.The aim of this thesis is to identify the barriers to VR implementation in neurorehabilitation and to elicit the specific requirements for its integration as an effective support tool. To achieve this goal, a systematic review of current applications was first conducted to identify methodologies and features associated with significant therapeutic outcomes. This analysis revealed considerable heterogeneity across clinical protocols, as well as the hardware and software used, highlighting the lack of standards and quality criteria that hinder result generalization. To address this gap, a new conceptual framework for evaluating VR tools in clinical contexts was developed: the Virtual Reality-tools Quality Assessment Framework (VR-tools QAF), which defines technical, functional, and safety requirements for VR equipment and virtual environments. In parallel, a comprehensive methodology for user-centred design, iterative development, and clinical validation was created, termed the Virtual Reality-tools Design and Development Guide (VR-tools DDG).Through the combined model (VR-tools QAF + DDG), eleven multimodal VR environments were developed to support the rehabilitation of cognitive and sensorimotor functions in individuals who have suffered an ABI. These environments integrate principles of repetitive practice, multisensory feedback, adaptive difficulty, and mechanisms for tailoring the experience to the patient’s profile. Validation was carried out in collaboration with a leading hospital in neurorehabilitation and brain health, where usability and feasibility tests were conducted with healthcare professionals and patients with ABI. Proof-of-concept trials demonstrated good acceptance and ease of use among clinicians (n = 26) from different specialties, as well as good tolerance and absence of adverse effects among the 20 patients. Moreover, a longitudinal VR intervention conducted within an efficacy study confirmed the feasibility and safety of delivering a 9-hour VR-based cognitive rehabilitation protocol with 21 patients with ABI, distributed in 20–30-minute sessions, 2–3 times per week.Overall, this research establishes a comprehensive methodological model for effectively integrating immersive VR into neurorehabilitation hospital settings, combining scientific rigour with technical and clinical feasibility. The results lay a solid foundation for future efficacy studies aimed at developing standardized treatment protocols for patients with ABI.
  

Reading date: 26/02/2026

• LAMICH, TOMÁŠ: A single emitter emitting resonance fluorescence into a coherent beam
  
  Author: LAMICH, TOMÁŠ
  Thesis file: (contact the Doctoral School to confirm you have a valid doctoral degree and to get the link to the thesis)
  Programme: DOCTORAL DEGREE IN PHOTONICS
  Department: Institute of Photonic Sciences (ICFO)
  Mode: Normal
  Deposit date: 15/01/2026
  Reading date: 26/02/2026
  Reading time: 10:00
  Reading place: ICFO Auditorium https://teams.microsoft.com/meet/35229889030806?p=LCcjpiwWFwQJd0iAQs
  Thesis director: MITCHELL, MORGAN | VEYRON, ROMAIN
  Thesis abstract: This thesis studies the statistics of light produced by a single trapped atom in free space when interfaced with two orthogonal coherent beams. The atom scatters light into the same spatial mode as a weak coherent probe beam, giving rise to controllable photon statistics. Being able to control the photon statistics of a source can be used in applications in where different photon statistics are desired.A single emitter in free space, when left to interact with a single pumping beam can only scatter one photon at any given moment leading to anti-bunched photon statistics. However, Goncalves et al. (2021) studied the possibility of interfacing the atom with a strong pumping beam, and a weak probing beam, leading to a controllable photon statistics, where super- and sub-Poissonian statistics can be achieved by varying either the pump-probe ratio or the relative pump-probe phase. By controlling the pump-probe ratio, it is also possible to control the probe transmission through the atom.The experimental implementation of the "GMC" scheme shows the predicted behaviour where the transmission can be suppressed to 62 %, and tuned by changing the pump-probe ratio. It also shows that the photon statistics can go from super- to sub-Poissonian by changing the relative pump-probe phase, and the photon bunching achieved is also pump-probe ratio dependent.In addition, measurements of the atom temperature are presented in this thesis, where the interference of the pump and probe beams on the atom lead to a direct measurements of the rms displacement of the atom within the trap, which is linked directly to the atom temperature. These measurements demonstrate a new non-destructive method of estimating the atom temperature.
  

Reading date: 27/02/2026

• BRAGANTINI, ANDREA: Learning-based State Estimation for Low Voltage Distribution Grids Using Neural Networks
  
  Author: BRAGANTINI, ANDREA
  Thesis file: (contact the Doctoral School to confirm you have a valid doctoral degree and to get the link to the thesis)
  Programme: DOCTORAL DEGREE IN ELECTRICAL ENGINEERING
  Department: Department of Electrical Engineering (DEE)
  Mode: Normal
  Deposit date: 20/01/2026
  Reading date: 27/02/2026
  Reading time: 14:00
  Reading place: Aula Capella - ETSEIB Enlace meet público: meet.google.com/qba-hxad-kzb
  Thesis director: SUMPER, ANDREAS
  Thesis abstract: This doctoral thesis investigates how learning-based methods, particularly artificial neural networks (ANNs), can provide a practical and cost-effective solution for state estimation in low-voltage (LV) distribution grids, where measurement infrastructure is typically scarce or absent. Traditional state estimation (SE) approaches, such as Weighted Least Squares (WLS), are effective at transmission and, with adaptations, at medium-voltage level, but are economically and technically unfeasible for LV networks. Their radial topology, unbalanced operation, high R/X ratios, and large number of nodes make dense metering and communication systems prohibitive for distribution system operators (DSOs).This work proposes a simulation-driven methodology to train ANN-based state estimators (ANNSEs) capable of monitoring LV grids in near real time using a limited set of synchronized measurements, primarily those available at secondary transformer substations.The research begins by defining a standardized design and evaluation framework for ANNSEs. Monte Carlo–based probabilistic power flow simulations are used to generate synthetic training datasets from minimal grid information. Initially, a single-phase approximation is adopted, and ANN models are trained to map four basic substation measurements (voltage, current, active and reactive power) to nodal voltage magnitudes. A bi-dimensional performance assessment combining mean absolute error (MAE) and R² is introduced. Case studies on rural, village, and suburban networks show MAE values typically below 1 V, with higher robustness in larger grids exhibiting stronger voltage gradients. Input voltage measurement accuracy emerges as the main limiting factor.The methodology is then benchmarked against alternative machine learning models, including linear regression, random forests, gradient-boosted trees, and multilayer perceptrons. While prediction accuracy remains comparable, custom feed-forward neural networks (FFNNs) demonstrate superior scalability and architectural flexibility, supporting their selection as the preferred ANNSE architecture.The core contribution extends the methodology to realistic three-phase, four-wire LV networks. A multi-network FFNN architecture with separate sub-networks for each phase and the neutral conductor is combined with a three-phase probabilistic power flow. Validation using real pilot data confirms accurate prediction of phase-to-ground voltages, including severe voltage drops and neutral voltage rise, with MAE below 0.3 V and down to 0.2 V when an additional measurement device is optimally placed.The final study addresses robustness and deployment challenges. Sensitivity analyses quantify the impact of grid-model inaccuracies, network evolution, and measurement errors. Moderate errors in line parameters, load growth (up to 9%), or PV penetration (up to doubling) only marginally affect performance, postponing retraining. In contrast, incorrect phase allocation and biased or missing voltage measurements significantly degrade accuracy. Local Sensitivity Analysis shows that ANNSE predictions are primarily anchored to voltage inputs, while power and current measurements contribute locally within feeders, yielding physically interpretable sensitivity patterns.Taken together, these studies define a coherent methodology for deploying ANN-based state estimators as a scalable and cost-effective monitoring tool for LV distribution grids. The thesis demonstrates that ANNSEs, trained offline on digital twins with simulation-driven data, can be deployed in practice to detect voltage deviations and fluctuations in near real time using minimal instrumentation, enhancing observability and reliability in currently unmonitored LV networks.
  

• CASTRO CARRASCO, REBECA IGNACIA: Characterization of sulfate-reducing biofilms using an amperometric printed H₂S sensor
  
  Author: CASTRO CARRASCO, REBECA IGNACIA
  Thesis file: (contact the Doctoral School to confirm you have a valid doctoral degree and to get the link to the thesis)
  Programme: DOCTORAL DEGREE IN NATURAL RESOURCES AND THE ENVIRONMENT
  Department: Department of Mining, Industrial and ICT Engineering (EMIT)
  Mode: Normal
  Deposit date: 22/01/2026
  Reading date: 27/02/2026
  Reading time: 10:00
  Reading place: Sala d'Aces EPSEM
  Thesis director: GABRIEL BUGUÑA, GEMMA | GUIMERÀ VILLALBA, XAVIER
  Committee:
       PRESIDENT: JIMENEZ JORQUERA, CECILIA
       SECRETARI: SOLE SARDANS, M. MONTSERRAT
       VOCAL: MORA GARRIDO, MABEL
  Thesis abstract: A comprehensive understanding of sulfidogenic processes in bioreactors remains incomplete by the limited availability of tools suitable for the sulfate-reducing activity characterization of immobilized biomass. To address this limitation, the present work is based on developing suitable alternatives for sulfate reducing biomass characterization using electrochemical microsensors. In this sense, a flow-cell bioreactor was developed for real-time monitorization using artificially immobilized biomass to substitute the natural immobilization derived from extracellular polymeric compounds. Physical and functional evaluations enabled the identification of a polymer–biomass matrix capable of preserving sulfate-reducing performance while ensuring adequate microbial retention and structural integrity, as well as a range of operational conditions was assessed to generate detailed H₂S production profiles within the flow-cell bioreactor. In parallel, an inkjet-printed H₂S microsensor was fabricated on polyethylene terephthalate substrates using silver and gold inks and modified with Single-walled carbon nanotubes reinforced with Polyvinyl alcohol and Polydiallyldimethylammonium chloride, which improved ink dispersion, adhesion, and mechanical stability. The optimized formulation yielded long-term operational stability, linear responses across different media, and performance comparable to commercial microsensors despite an initial decrease in sensitivity. Furthermore, the study evaluated the operational behavior of artificial sulfate-reducing granules in column and continuous stirred tank reactors, demonstrating high sulfate removal efficiencies at moderate loading rates, superior stability in column configurations, accumulation of volatile fatty acids associated with incomplete glycerol oxidation, and the effectiveness of a bioaugmentation strategy based on acetate-oxidizing sulfate reducing bacteria immobilized in artificial granules. Lastly, the integrated platform was validated for the analysis of H₂S production in immobilized sulfate-reducing biofilms, combining the flow-cell bioreactor with direct ink writing printed microsensors for simultaneous, in situ monitoring of H₂S and pH. Three-dimensional mapping revealed pronounced H₂S gradients driven by mass-transfer limitations and hydrodynamic dispersion, while printed electrodes exhibited linear amperometric responses and stable performance over extended operation, thereby confirming the suitability of the proposed platform for high-resolution, real-time characterization of sulfidogenic biofilms and immobilized sulfate-reducing biomass.
  

• CHASCO GOÑI, UXUE: Innovative techniques for the 3D numerical simulation of high mountain torrent flows.
  
  Author: CHASCO GOÑI, UXUE
  Thesis file: (contact the Doctoral School to confirm you have a valid doctoral degree and to get the link to the thesis)
  Programme: DOCTORAL DEGREE IN CIVIL ENGINEERING
  Department: Barcelona School of Civil Engineering (ETSECCPB)
  Mode: Normal
  Deposit date: 23/12/2025
  Reading date: 27/02/2026
  Reading time: 12:00
  Reading place: Sala Zienkiewich (CIMNE) Building C1, UPC - Campus North Gran Capitan S/N 08034 Barcelona
  Thesis director: ROSSI BERNECOLI, RICCARDO | ZORRILLA MARTÍNEZ, RUBÉN
  Thesis abstract: This thesis develops a numerical tool for the analysis of torrential flows in high-mountain area. The formulation is based on an Eulerian two-fluid, Newtonian incompressible approach combined with a level set method for capturing the free surface.One of the main contributions of the thesis is the improvement of the mass-preserving and energy-preserving properties of Eulerian two-fluid formulations. A consistent mass source term is added to adress the intrinsic mass losses of the level-set method, and a three-step splitting strategy is introduced to guarantee the energy-preserving properties of the numerical scheme for the coupled Navier–Stokes and free-surface convection problem.The formulation is also extended to non-Newtonian rheologies, providing the capability to reproduce the more complex flow behaviours exhibited during mass flow events. A method is proposed that adapts standard CFD boundary conditions within a two-fluid framework to hydraulic flows, allowing both supercritical and subcritical regimes to be accurately captured.A black-box tool for generating three-dimensional terrain meshes is also developed, producing geometries derived from real terrain data and enabling its application to mass flow hazard scenarios.The proposed framework is validated through theoretical, experimental, and real-scale cases. Among these cases, the glacier–rock collapse in Chamoli (India, 2021) is especially significant, as it demonstrates the capability of the developed tool to reproduce a large scale torrential event and confirms its suitability for high mountain mass flow hazard analysis.