Theses authorised for defence

DOCTORAL DEGREE IN APPLIED MATHEMATICS

• GONZALEZ HERNANDEZ, LAURA: On families of prime ideals with an unbounded minimal number of generators in a three-dimensional power series ring
  
  Author: GONZALEZ HERNANDEZ, LAURA
  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 APPLIED MATHEMATICS
  Department: School of Mathematics and Statistics (FME)
  Mode: Normal
  Deposit date: 27/01/2026
  Reading date: 27/03/2026
  Reading time: 16:00
  Reading place: Sala d'Actes de l'FME, Edifici U, Campus Sud
  Thesis director: PLANAS VILANOVA, FRANCESC D'ASSIS
  Thesis abstract: This thesis deals with the existence of families of prime ideals in the power series ring k[[x,y,z]] with an unbounded minimal number of generators.We begin by studying in-depth the related results of Moh on the area. We reprove and generalize a result of Moh which gives a lower bound on the minimal number of generators of an ideal in k[[x,y,z]]. In doing so, we demonstrate that the minimal number of generators of Moh’s prime P3 might decrease depending on the characteristic of the field k. This result contradicts a previous statement made by Sally and leaves as an open problem finding families of prime ideals in k[[x,y,z]] with an unbounded minimal number of generators, when the characteristic of k is different from zero. The main result of this thesis is the construction of a new family of prime ideals in k[[x,y,z]] with an unbounded minimal number of generators, explicitly described, up to constant coefficients, which improves all the former results. The construction and analysis of these families rely on the theory of numerical semigroups and the study of binomial matrices.We first study the numerical semigroup S spanned by three consecutive natural numbers, a,a+1,a+2. We define and characterize the set of elements whose factorizations have all the same length, ULF(S), We provide an explicit description of their factorization sets and a natural partition based on the length and the denumerant. Moreover, by using Apéry sets and Betti elements, we are able to extend some of these results to any general numerical semigroup G. These findings link the structural properties of S directly to the defining ideals of the semigroup rings k[t^a,t^b,t^c], providing a bridge between factorization theory and the minimal generating sets of the corresponding prime ideals.In addition to our particular study of the numerical semigroup S, we need to work with binomial matrices. We derive closed formulae for binomial determinants and calculate bases to left nullspaces of some special binomial matrices. Additionally, we provide an alternative proof for the positivity of binomial determinants, originally shown by Gessel and Viennot. Finally, we display our new family of prime ideals with unbounded minimal number of generators in k[[x,y,z]], where k is a field of characteristic zero. These primes are obtained as the kernel of a quasi-monomial algebra homomorphism. Up to constant coefficients, we give a description of their minimal generating polynomial sets. The advantage of our family with respect to some previous work is the explicit description of the minimal generating sets and the simplicity of the exponents of the monomial presentation.
  

DOCTORAL DEGREE IN ARTIFICIAL INTELLIGENCE

• FERRANDO MONSONÍS, JAVIER: Interpretability in Natural Language Processing and Machine Translation
  
  Author: FERRANDO MONSONÍS, JAVIER
  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 ARTIFICIAL INTELLIGENCE
  Department: Department of Computer Science (CS)
  Mode: Normal
  Deposit date: 13/11/2025
  Reading date: 13/02/2026
  Reading time: 12:00
  Reading place: Sala MERIT D5010, Edifici D5, Campus Nord UPC, Barcelona
  Thesis director: RUIZ COSTA-JUSSA, MARTA
  Thesis abstract: This thesis presents a set of methods and analyses designed to improve our understanding of the internal mechanisms of Transformer-based models in natural language processing and machine translation.This work first investigates the role of attention weights in encoder-decoder Transformers, showing that while they do not provide accurate word alignments, they nonetheless help explain model predictions and contribute to a deeper understanding of translation quality.A central contribution of the dissertation is the development of ALTI and its extensions, which offer a new approach to input attribution. These methods challenge prior assumptions about the explanatory power of attention mechanisms and reveal how information propagates between encoder and decoder components. In doing so, they also shed light on sources of hallucinations in translation systems.Further, the thesis introduces techniques to attribute predictions to individual components and positions, enabling contrastive explanations of linguistic behavior. These explanations clarify how language models represent and solve different linguistic phenomena.The dissertation also proposes a methodology for tracking information flow during inference, offering insight into how various components contribute to model predictions. This allows for the identification of domain-specialized components and a better understanding of how representations are transformed across layers.Lastly, the analysis of cross-lingual circuit similarities reveals shared structural patterns in how models handle different languages. These findings point to potential universal mechanisms in language models.Collectively, this thesis advances the interpretability of Transformer models by providing tools and frameworks for probing, attributing, and understanding the behavior of complex NLP systems.
  

DOCTORAL DEGREE IN AUTOMATIC CONTROL, ROBOTICS AND VISION

• MEDRANO DÍAZ, MANUEL ALEJANDRO: Estimación de series de tiempo de imágenes mediante técnicas de aprendizaje profundo
  
  Author: MEDRANO DÍAZ, MANUEL ALEJANDRO
  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 AUTOMATIC CONTROL, ROBOTICS AND VISION
  Department: Department of Automatic Control (ESAII)
  Mode: Change of supervisor
  Deposit date: 20/01/2026
  Reading date: 19/02/2026
  Reading time: 16:00
  Reading place: Aula Maestría de Ciencias de la Computación, Instituto Tecnológico de Culiacán, MéxicoEnlace Videoconferencia: https://meet.google.com/cya-jyje-zeq
  Thesis director: PUIG CAYUELA, VICENÇ | RODRÍGUEZ RANGEL, HÉCTOR
  Thesis abstract: An image time series (ITS) is a chronologically ordered sequence of images showing the spatial change of its elements over time. Satellite images of meteorological events can be treated as ITS by displaying their values through pixel color intensity.Estimating from a ITS with a deep learning model is a complex problem that requires analyzing various configurations that determine how images are processed. It is a computationally intensive problem, which, due to its non-deterministic characteristics, requires combining different parameter configurations to extract spatio-temporal relationships. Thus, the complexity of the problem increases as the dimensions to be evaluated increase.To solve this problem, a robust and scalable conceptual model for ITS estimation is proposed that extracts the spatiotemporal relationships between pixels and their neighborhoods. Based on the specifications of the proposed model, a methodological proposal is developed that allows the estimation of meteorological maps using deep learning models. The proposed methodology is implemented through the design of a software architecture that translates abstract elements into software components, allowing the methodology to be evaluated through the use of deep learning models in different case studies.In the case study of the United States (US) drought monitor, experimentation with deep learning models based on ConvLSTM and Multi-CNN mostly yielded an F1-score of over 0.90 for the estimation of step t+1, with the best model obtaining an F1-score of 0.9953. Due to the high memory demand of the data dimensions, together with the physical limitations of the hardware equipment, dimension reduction techniques were applied to the images. Using the fragmentation technique with the ConvLSTM architecture, an F1-score of 0.9684 was obtained by reducing the dimension of the samples by 48%. By applying recursive and direct multi-step estimation strategies, medium-term estimates could be made. However, due to the complexity of the spatiotemporal analysis, there is an accumulated error that affects the quality of the medium-term estimate.In a second case study on US standardized precipitation index (SPI) maps, a ConvLSTM architecture is used to estimate step t+1. The results show that the best learning model obtains an F1-score of 0.5268, while the Naïve model obtains an F1-score of 0.3408. The results demonstrate the capabilities of deep learning models to extract spatiotemporal relationships from a sequence of images, laying the foundation for a branch of research focused on image estimation.
  
• VINARDELL MAGRE, LAURA: FULL-SCALE WATER DISTRIBUTION NETWORKS MODELLING FOR DISINFECTION BY-PRODUCTS ESTIMATION
  
  Author: VINARDELL MAGRE, LAURA
  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 AUTOMATIC CONTROL, ROBOTICS AND VISION
  Department: Department of Automatic Control (ESAII)
  Mode: Normal
  Deposit date: 09/01/2026
  Reading date: 20/02/2026
  Reading time: 11:00
  Reading place: Sala d'Actes de la seu d'EURECAT a Manresa, Plaça de la Ciència, 2, 08242 Manresa
  Thesis director: PEREZ MAGRANE, RAMON | JUBANY GUELL, IRENE
  Thesis abstract: The quality of the water that reaches our homes depends on multiple factors: the origin of the water, the treatment it undergoes, and the distribution network that delivers it. To ensure water safety not only at the treatment plant’s outlet but also at the points of consumption, chemical disinfectants are often added. Chlorine is one of the most widely used disinfectants worldwide due to its low cost, versatility, and its ability to maintain residual disinfectant power throughout the network. However, despite these advantages, chlorine can form potentially harmful by-products under certain conditions. For this reason, the latest European Union directive (2023) mandates the regulation of several disinfection by-products: trihalomethanes (THMs), haloacetic acids (HAAs), chlorate, chlorite, and bromate.This thesis presents the development of several models to estimate the concentration of by-products resulting from the use of hypochlorite as a disinfectant. Two case studies have been examined: one high-pressure distribution network and another with both high and low-pressure distribution. Over the course of a year, multiple sampling campaigns were carried out to collect sufficient data to calibrate and validate the estimation models. Prior to this, hydraulic models were adjusted to provide input for the water quality models.The study focuses on three types of disinfection by-products regulated by European standards: the THM family, the HAA family, and chlorate. It was observed that the speciation of THMs and HAAs is strongly influenced by the water source. Moreover, the evolution of the concentration of these compounds varies depending on network operation and the number of rechlorination stations. For chlorate, its formation was studied in relation to the use and storage of concentrated hypochlorite, both in primary disinfection and in rechlorination stations.Two modelling approaches were applied for THMs and HAAs: mechanistic models and data-driven models. The mechanistic models are based on the relationship between compound formation and chlorine decay, whereas data-driven models identify patterns among variables without explicitly relying on the underlying physical or chemical processes. In the latter case, specific models were developed for each case study, as well as a general model applicable to both networks, despite their differences in size, water origin, and operation.All models developed for THMs and HAAs were applied both to individual compounds and to the regulated totals (TTHMs and THAAs). The study assessed which strategy yields better estimations: summing individual models or directly modelling the total concentrations. In this study, direct estimation of totals gave slightly better results. However, modelling individual compounds can provide added value, both from a risk management perspective (since each compound may have a different impact) and for a deeper understanding of their formation mechanisms.Finally, for chlorate, a mechanistic model was developed based on the known kinetics of hypochlorite degradation. The formation of chlorate in concentrated hypochlorite storage tanks was integrated into a hydraulic model simulating the behavior of the water tank receiving chlorination, enabling estimation of the chlorate concentration at the outlet. The model provided accurate predictions and can be applied to other distribution networks.
  

DOCTORAL DEGREE IN CIVIL ENGINEERING

• 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.
  

DOCTORAL DEGREE IN COMPUTER ARCHITECTURE

• SAMADI GHARAJEH, MOHAMMAD: Mapping of Real-Time Computation to Parallel Platforms
  
  Author: SAMADI GHARAJEH, MOHAMMAD
  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 COMPUTER ARCHITECTURE
  Department: Department of Computer Architecture (DAC)
  Mode: Normal
  Deposit date: 20/01/2026
  Reading date: 19/02/2026
  Reading time: 11:00
  Reading place: C6-E101
  Thesis director: PINHO, LUIS MIGUEL | ROYUELA ALCÁZAR, SARA
  Thesis abstract: Time-critical systems are a special type of application that must complete computing tasks within specific time constraints to guarantee the required level of service. As these systems have become more complex in recent years, they have increased performance requirements, which necessitates the need to use more powerful hardware platforms, providing higher performance. The performance of these systems can be improved by utilizing multi-core processors and parallel programming models (e.g., OpenMP). However, the predictability and schedulability of these parallel applications easily become intricate due to the potentially complex structure of execution graphs and the several resources available in modern architectures. Therefore, mapping tasks to computing resources needs to be performed efficiently in these applications to improve the work-conserving of the mapping process and the load-balancing of the job queues. This process can lead to reduced application response time (and WCRT), as well as its variability.To address the problems mentioned above, this thesis tackles the challenge of maintaining system predictability and schedulability while maximizing performance in real-time parallel computing systems. Accordingly, it (i) proposes task-to-thread mapping methods based on heuristics that exploit knowledge about the behavior of predictable parallel applications, considering both the offline design of the system as well as the online execution phase, to reduce the variability of response times and the WCRT, as well as to improve the average response time in OpenMP applications, (ii) uses efficient methods for measuring parallel tasks in terms of schedulability and predictability as well as discovering the longest execution path through the parallel execution (i.e., WCRT), (iii) evaluates the impact of the configurations of mapping algorithms (e.g., static and dynamic mapping) and hardware platforms on the task execution time and application response time, and (iv) proposes and evaluates the performance of task-to-accelerator mapping using different heuristics to reduce the response time by executing high-workload tasks in the accelerator.Evaluation results, based on simulations and experiments using random graphs and real-world applications, demonstrate that the new approaches, in most cases, minimize response time (and WCRT) and reduce the variability of response times compared to existing mapping approaches. In addition, a prototype implementation of the main heuristics is evaluated using real-world applications, showing that the WCRT and response time variability obtained using the new methods are lower than those obtained using LLVM's default scheduler in most configurations. These achievements show that the proposed methods can improve the predictability and schedulability of real-time parallel applications.
  

DOCTORAL DEGREE IN ELECTRICAL ENGINEERING

• 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
  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.
  
• NOLASCO BENITEZ, EDITH: Evaluación de programas de electrificación rural desde un enfoque de sostenibilidad: Análisis del impacto del BID-FERUM II en Ecuador
  
  Author: NOLASCO BENITEZ, EDITH
  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: 20/03/2026
  Reading time: 10:00
  Reading place: Aula 28.8 Sala de Conferències de l'ETSEIB
  Thesis director: GOMIS BELLMUNT, ORIOL
  Thesis abstract: Access to energy is a key driver of economic and social development, as it fosters income generation, contributes to poverty eradication, and enhances quality of life. Its significance has been acknowledged through initiatives such as the 2030 Agenda, which prioritises universal access to clean and modern energy. Nevertheless, despite notable progress, millions of people worldwide remain without access to electricity services, predominantly in rural areas of developing countries. Africa is among the most affected regions, while others, such as Latin America, the Caribbean, and East Asia, have made substantial progresses towards universal access. Factors such as the pandemic, inflation, and the global energy crisis have slowed this progress, impacting equity and sustainability in energy access.In response to this scenario, rural electrification has been approached through various technical strategies, including grid extension, stand-alone systems, and mini-grids. However, significant challenges persist regarding the sustainability and effectiveness of implemented programmes. The need for comprehensive evaluations has led to the development of methodological frameworks focused on energy sustainability.This doctoral thesis aims to evaluate a rural electrification programme using a methodology based on a sustainability framework comprising five dimensions: technical, economic, social, environmental and institutional. The evaluation is conducted at the local level, using a case study the Rural and Urban-Marginal Electrification Programme FERUM II, implemented in the province of Esmeraldas, Ecuador. Through this territorial analysis, key aspects for evaluating such programmes are identified, and specific indicators are defined to assess the achievement of objectives within each dimension. The proposed methodology seeks to offer a practical, contextualised, and replicable guide for developing countries interested in conducting rigorous evaluations of rural electrification initiatives.The research is structured in several phases: first, a systematic literature review to contextualise the problem and build the theoretical framework; then, the development of the evaluation methodology, applied to the Ecuadorian case through semi-structured interviews, questionnaires and fieldwork in rural communities. Based on the analysis of locally collected data, two fundamental questions are addressed: What elements should be considered when evaluating rural electrification programmes? and What outcomes emerge from applying the sustainability framework to the FERUM II case?Preliminary results indicate that the programme has led to substantial improvements in access to electricity services and in the quality of life of the beneficiary population in Esmeraldas. This demonstrates that the proposed sustainability framework effectively captures the multidimensional impacts of rural electrification in vulnerable contexts. The economic dimension reveals certain weaknesses, particularly regarding the promotion of productive activities and access to credit. In contrast, the social dimension shows a positive impact, with progress in education, communications, and security. From an environmental perspective, a reduction in the use of fossil fuels is observed due to the incorporation of grid electricity. Regarding the institutional component, although performance is generally acceptable, deficiencies are identified in the relationship between distribution companies and beneficiary communities, particularly in terms of user support and communication.Keywords: Rural electrification, energy sustainability, programme evaluation, energy access, social and economic development, sustainable development, Esmeraldas, Ecuador.
  

DOCTORAL DEGREE IN ELECTRONIC ENGINEERING

• ROSERO POZO, CARLOS ALBERTO: CONTRIBUTION TO THE DESIGN OF INSTRUMENTATION FOR THE SURFACE AND SUBSURFACE OF MARS
  
  Author: ROSERO POZO, CARLOS ALBERTO
  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 ELECTRONIC ENGINEERING
  Department: Department of Electronic Engineering (EEL)
  Mode: Normal
  Deposit date: 22/01/2026
  Reading date: 19/03/2026
  Reading time: 11:00
  Reading place: Aula de Teleensenyament, edifici B3
  Thesis director: DOMINGUEZ PUMAR, MANUEL MARIA | JIMENEZ SERRES, VICENTE
  Thesis abstract: Accurate in situ characterization of the atmospheric environment and its interaction with planetary surfaces, as well as the thermophysical properties of those surfaces, is essential for understanding surface–atmosphere processes, supporting the development of climate predictive models, and enabling the design of exploration systems for robotic and human missions. On Mars, these measurements are strongly constrained by low atmospheric pressure, large temperature gradients, while current missions have strict limitations associated to mass, power and autonomy. This thesis addresses these challenges through the analysis, modeling, and validation of instrumentation and methodologies for planetary surface exploration.The main contribution of this work is the adaptation and validation of a wind sensor structure as a spherical thermoprobe for the characterization of regolith thermophysical properties based on the thermal impedance concept. By injecting a controlled periodic thermal excitation and analyzing the temperature response in the frequency domain, the proposed approach enables the extraction of thermal conductivity and thermal diffusivity. A detailed and realistic analytical thermal model is developed, including probe-specific parameters to improve sensor performance, together with the implementation of compensation techniques for structural heat losses, allowing the capture of the most relevant thermal impedance spectra over an extended frequency range. The methodology is experimentally validated through measurements using hollow glass microbeads and the MMS-2 simulant, the latter being representative of realistic Martian conditions, and showing good agreement with reference instruments.In addition, this thesis investigates the thermal behavior of the Martian wind sensor of the MEDA instrument, with emphasis on the supporting structures that interact with the surrounding atmosphere. The one-dimensional analytical thermal model used to describe the sensor response is evaluated through three-dimensional finite element simulations under low-density atmospheric conditions. The applicability of different convective heat transfer correlations is analyzed, showing that rarefaction effects play a key role in slender structures such as bond-wires. The results confirm the validity of the simplified analytical formulation and provide improved insight into heat transfer mechanisms in Martian wind sensing probes.Overall, this work contributes to improving the reliability of in situ measurements and to the development of compact solutions suitable for integration on landers, rovers, and small robotic platforms for future Mars surface missions or other celestial bodies.
  

DOCTORAL DEGREE IN GEOTECHNICAL ENGINEERING

• ALONSO, MATÍAS: Hydro-mechanical modelling of a sealing concept for a deep geological radioactive waste repository
  
  Author: ALONSO, MATÍAS
  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 GEOTECHNICAL ENGINEERING
  Department: Department of Civil and Environmental Engineering (DECA)
  Mode: Normal
  Deposit date: 15/12/2025
  Reading date: 16/03/2026
  Reading time: 11:00
  Reading place: ETSECCPB.UPC, Campus NordBuilding C1. Classroom: 002C/Jordi Girona, 1-308034 Barcelona
  Thesis director: VAUNAT, JEAN | OLIVELLA PASTALLE, SEBASTIAN
  Thesis abstract: Deep Geological Disposal (DGD) has emerged as the most viable solution for the final disposal of radioactive waste, offering the potential for the permanent containment and isolation of waste from the biosphere over extended timescales. Several countries have made significant progress in developing Deep Geological Repository (DGR) concepts for the permanent disposal of such waste. The long-term safety of these facilities relies primarily on the host rock—the natural barrier that plays the central role—supplemented by engineered components collectively referred to as the engineered barrier system (EBS). The EBS includes containers, backfills, buffers, and other structures designed to ensure favourable conditions for the long-term isolation of radioactive waste. The design, performance, and safety assessment of a DGR—and particularly of its EBS components—are therefore essential for the sustainable development of nuclear energy, making their study a key research area within geotechnical engineering.In this context, the main objective of this research is to contribute to the understanding and assessment of the long-term performance of a large-diameter sealing concept developed within the framework of the Cigéo project, led by the French National Radioactive Waste Management Agency (Andra). To achieve this objective, a multi-scale and multi-step numerical modelling strategy has been adopted. The approach combines detailed material characterisation with advanced constitutive modelling of the expansive core, backfill materials, and host rock, accounting for features such as inherent anisotropy and double structure. The modelling framework incorporates coupled hydro-mechanical processes, enabling the analysis of key phenomena such as the natural hydration of the sealing core, the development of swelling pressure, the resaturation and recompression of the excavation-damaged zone (EDZ), the global equilibrium of the sealing system, and the potential deconfinement of the sealing core and its associated loss of swelling capacity. The simulations address the complexity of the problem by integrating large-scale three-dimensional geometries, advanced constitutive formulations, and critical geometric details at the decimetre scale. These challenging simulations provide valuable insights into the performance and long-term integrity of the sealing structures, establishing a robust framework and a powerful tool to enhance the understanding of the behaviour of these EBS, contributing to the optimisation of repository design and safety.
  
• ESCANELLAS TUR, ANDREU: Time-dependence of rock salt fracture mechanics: Experimental an Numerical study
  
  Author: ESCANELLAS TUR, ANDREU
  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 GEOTECHNICAL ENGINEERING
  Department: Department of Civil and Environmental Engineering (DECA)
  Mode: Normal
  Deposit date: 26/01/2026
  Reading date: pending
  Reading time: pending
  Reading place: pending
  Thesis director: CAROL VILARASAU, IGNACIO | LIAUDAT, JOAQUÍN
  Thesis abstract: This thesis presents an experimental and numerical study on the time-dependent behaviour of fracture propagation in rock salt. The research aims to enhance the understanding of rock salt fracture mechanics, which is crucial for applications such as underground storage of hazardous waste and energy storage, whose long-term stability remains difficult to predict due to the combined effects of creep and fracture. Despite its practical significance, studies addressing the combined effects of creep and Fracture Mechanics of rock salt are scarce in the literature. To address this gap, an experimental campaign was conducted on halite specimens extracted from the Cardona Formation in Spain. Wedge Splitting Tests (WST) were performed at four loading rates spanning three orders of magnitude, complemented by uniaxial creep tests at stress levels between 2.5 and 10 MPa. The WST results reveal a clear rate dependency: peak splitting forces increase with loading rate, while the mechanical work required for fracture decreases, indicating significant contributions of creep to the fracture process at slower rates. The creep tests show non-linear viscous deformation, with approximately linear behaviour only at low stress levels. Subsequently, numerical simulations were performed using the in-house developed finite element code, which combines interface elements with an elasto-plastic constitutive law and visco-elastic continuum elements. These initial simulations partially reproduced the observed experimental trends, confirming that both bulk creep and fracture-process-zone mechanisms contribute to the observed rate dependency, but failing to match quantitatively the experimental results. To improve the modelling capabilities, two new constitutive laws for interface elements are proposed, incorporating elastic degradation, frictional behaviour, viscoelasticity and viscous dissipation processes. These formulations enhance the representation of time-dependent fracture by enabling consistent modelling of salt fracture across a wide range of loading rates.Simulations performed with the new proposed visco-elastic-damage model show improved agreement with experimental results, quantitatively reproducing the observed trends in apparent fracture energy and peak force with regard of loading rate.
  
• RODRÍGUEZ ROMERO, CARLOS EDUARDO: Analysis of coupled hydro-mechanical processes in double-structure geomaterials for nuclear waste storage
  
  Author: RODRÍGUEZ ROMERO, CARLOS EDUARDO
  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 GEOTECHNICAL ENGINEERING
  Department: Department of Civil and Environmental Engineering (DECA)
  Mode: Normal
  Deposit date: 04/12/2025
  Reading date: 19/02/2026
  Reading time: 11:00
  Reading place: ETSECCPB. UPC, Campus NordBuilding C2. Classroom: 212C/Jordi Girona, 1-308034 Barcelona
  Thesis director: VAUNAT, JEAN | GENS SOLE, ANTONIO
  Thesis abstract: The safe long-term isolation of high-level radioactive waste requires engineered barriers capable of maintaining low permeability and mechanical stability under complex thermo-hydro-mechanical (THM) conditions. Among candidate materials, compacted bentonite exhibits a distinctive double-structure behaviour, governed by the coexistence of micro- and macro-porous domains. This thesis focuses on the analysis of coupled hydro-mechanical processes in double-structure geomaterials, with particular attention to bentonite mixtures of blocks and pellets, as used in buffer systems for deep geological repositories. The research first reviews the geomechanical basis of double-structure soils and identifies the experimental evidence supporting their dual-porosity nature. A constitutive THM framework is then developed, extending the existing double-structure formulation to incorporate: (i) the parameter ακ to control microstructural deformation; (ii) a fabric-dependent structuration law to represent the memory and degradation of compression; and (iii) frictional resistance at block–pellet and block–wall interfaces.The model was implemented and calibrated using laboratory and mock-up experiments from the BEACON project, including the MGR22, MGR23, and MGR27 experiments, the EPFL path-dependent tests and the POSIVA test. Numerical simulations successfully reproduced the evolution of swelling pressure, void ratio, dry density, water content and water intake observed experimentally. The results confirmed that friction plays a decisive role in the redistribution of stresses between pellets and blocks, while microstructural evolution governs the long-term homogenisation process. The enhanced formulation captured partial density homogenisation and the persistence of microstructural porosity, in agreement with laboratory observations.Overall, the thesis provides an improved understanding of the coupled hydro-mechanical behaviour of double-structure bentonites and proposes a robust constitutive framework capable of reproducing their key features under repository-relevant conditions. The work highlights the necessity of considering both microstructural evolution and frictional effects in predictive models for bentonite barriers, thus contributing to the reliability of long-term safety assessments of deep geological repositories.
  

DOCTORAL DEGREE IN NATURAL RESOURCES AND THE ENVIRONMENT

• 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: pending
  Reading time: pending
  Reading place: pending
  Thesis director: GABRIEL BUGUÑA, GEMMA | GUIMERÀ VILLALBA, XAVIER
  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.
  

DOCTORAL DEGREE IN OPTICAL ENGINEERING

• CUELLAR SANTIAGO, FATIMA: Optical and visual quality of presbyopia-correcting intraocular lenses
  
  Author: CUELLAR SANTIAGO, FATIMA
  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 OPTICAL ENGINEERING
  Department: Department of Optics and Optometry (OO)
  Mode: Article-based thesis
  Deposit date: 22/12/2025
  Reading date: 20/02/2026
  Reading time: 11:30
  Reading place: Auditori Joan Salvadó del Ce (CUV) Facultat d'Òptica i Optometria de TerrassaAvgda. 22 de Juliol, 660. 08222 Terrassameet.google.com/wbu-qxqi-hkj
  Thesis director: MILLAN GARCIA VARELA, MARIA SAGRARIO
  Thesis abstract: The development of new presbyopia-correcting intraocular lens (IOL) designs, with their manufacturing and launching on the market makes optical characterization highly recommendable, allowing for a more objective and quantitative understanding of their properties before implantation, thus avoiding the influence of subjective and individual factors. International regulations require the evaluation of IOL optical and visual quality in both laboratory and clinical settings.This investigation focuses on the interaction of new presbyopia-correcting IOLs designs with ocular optical aberrations (in normal and astigmatic corneas), the effect of IOL decentration and tilt on image quality, and the potential risk of unwanted optical phenomena. In all these cases, studying the optical behavior of a set of diffractive trifocals, extended-depth-of focus (EDOF), and enhanced monofocal (EM) IOLs under controlled in vitro conditions serves as a valuable complement to clinical studies. The optical quality of IOLs is evaluated through preclinical metrics based on the modulation transfer function (MTF), the estimation of postoperative visual acuity, range of vision and halo. Characterization was conducted using an optical bench equipped with a model eye consisting of a saline-filled wet cell in which the commercial presbyopia-correcting IOLs under study are immersed one by one. Adaptive optics was used to introduce controlled amounts of corneal aberration. This configuration allows for an objective, patient-independent assessment and enables control over factors such as pupil size, corneal aberrations, and lens alignment, aspects difficult to control in clinical practice. The experimental setups approximately reproduced the conditions under which the lens is implanted in the eye, in accordance with current international standards (ISO 11979-2-2024 and ANSI Z80.35-2018). An upgrade of the optical setup to allow for depth of field scanning in the object space has been initiated with the characterization of tunable focus lenses (FTL).To complement and validate the characterization of intraocular lenses (IOLs) in the optical bench, this work also includes studying visual quality using the SimVis Gekko visual simulator, which allows dynamic reproduction of vision with different intraocular lens designs before the cataract surgery. We also provide clinical results, obtained through collaborative research, that demonstrate excellent agreement with the laboratory findings. These data include visual assessments (with and without astigmatism) of pseudophakic patients implanted with the same presbyopia-correcting IOLs tested in the optical laboratory.
  
• LARROSA EXPÓSITO, MANEL: Viabilidad de un nuevo diseño de lentes de contacto de gran diámetro para ojos con queratocono. Estudio clínico.
  
  Author: LARROSA EXPÓSITO, MANEL
  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 OPTICAL ENGINEERING
  Department: Department of Optics and Optometry (OO)
  Mode: Normal
  Deposit date: 22/12/2025
  Reading date: 05/03/2026
  Reading time: 11:30
  Reading place: Auditori del Centre Universitari de la Visió
  Thesis director:
  Thesis abstract: Introduction:Keratoconus is an ocular disease that affects millions of people worldwide and represents a major cause of visual disability. Contact lens fitting is the most widely used strategy for correcting the refractive errors it induces, as it has proven to be an effective and safe option. However, no currently available lens design provides fully satisfactory results in all cases.Work performed:The main objective of this thesis was to evaluate the efficacy and safety of a new large-diameter rigid corneal contact lens design, featuring peripheral corneal support and vaulting over the cone, while simultaneously optimizing the personalized lens fitting process.In an initial study, the suitability of the corneal periphery as a bearing zone was assessed. To this end, the symmetry of revolution of the cornea in eyes with keratoconus and in healthy eyes was analysed based on a sagittal height measurements. The results indicated that the symmetry of revolution in the peripheral corneal region was comparable between both groups, supporting the feasibility of the proposed design with regard to the lens bearing area.In a second study, a prospective clinical trial was conducted to evaluate the outcomes after one year of lens wear in eyes with keratoconus. The analysis showed efficacy and safety levels comparable to those of other designs, a high level of user satisfaction, and, ultimately, high fitting and retention rates.Finally, a trial lens set based on the new design was developed and preliminarily validated thorough simulated fittings in eyes with keratoconus of different severities.Conclusions:The results obtained in this thesis demonstrate that the new large-diameter corneal lens design is a safe and effective option for visual correction in eyes with keratoconus.
  

DOCTORAL DEGREE IN PHOTONICS

• BELSA CARNÉ, BLANCA MARIA: Engineering Catalyst-Ionomer Interfaces for Carbon-Efficient CO2 Electrolysis and Technology Prospects
  
  Author: BELSA CARNÉ, BLANCA MARIA
  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: 20/02/2026
  Reading time: 10:30
  Reading place: ICFO Auditorium
  Thesis director: GARCÍA DE ARQUER, FRANCISCO PELAYO
  Thesis abstract: The electrochemical reduction of CO2 (CO2E) offers a promising route to convert greenhouse gas emissions into value-added chemicals and fuels. However, achieving performance metrics that enable the technoeconomic and sustainable viability of CO2E remains challenging. This is especially acute in the case of multicarbon products (C2+), important precursors for energy fuels and manufacturing, where achieving combined selectivity and carbon utilisation under industrially relevant conditions is challenged by undesired competing reactions. This thesis explores the design and implementation of new strategies to modulate electrochemical interfaces in CO2E to overcome this barrier. These are based on the implementation of ionomer coatings that specifically address key reactants and intermediates in CO2E. A key contribution is the development and mechanistic elucidation of ion management channels (IMCs), formed by co-distributing cation and anion exchange ionomers (CEIs and AEIs) within the catalyst layer. This architecture enables local regulation of hydroxide and cation populations, mitigating *OH poisoning and enhancing *CO adsorption, critical steps for promoting C–C coupling and C2+ product formation.The ionomer–catalyst interface is comprehensively characterised using SEM–EDS, FTIR, XPS, KPFM, contact angle measurements, cyclic voltammetry, and EIS. In situ Raman spectroscopy reveals the dynamic evolution of surface species, confirming that excessive *OH accumulation suppresses C2+ selectivity, while IMCs restore favourable interfacial conditions. These insights are correlated with improved electrochemical performance, carbon efficiency, and stability across a wide range of operating conditions, including highly acidic environments.The IMC concept is further implemented in membrane electrode assembly (MEA) devices operating under neutral pH. Preliminary results demonstrate improved performance and reduced cell voltages for IMC-based electrodes, indicating compatibility with scalable reactor platforms and commercially viable components.The thesis concludes with a broader analysis of the challenges facing CO2E at scale. Key bottlenecks, such as the reliance on iridium anodes and fluorinated membranes, are critically assessed, and material and performance targets for gigaton-scale deployment are proposed. A techno-economic and life-cycle analysis outlines trade-off between performance, cost, and sustainability, while global scaling efforts are reviewed. Benchmarking protocols are proposed to bridge the gap between laboratory research and industrial implementation.Together, this work advances a cohesive framework for interfacial engineering in CO2E, linking molecular-level understanding to device-scale integration, and providing pathways toward industrial deployment.
  
• 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.
  
• WRAGG, JOSEPH ALAN WINDLEY: Action Microspectroscopy for Nanophotonics
  
  Author: WRAGG, JOSEPH ALAN WINDLEY
  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: 28/01/2026
  Reading date: 27/02/2026
  Reading time: 10:00
  Reading place: ICFO Auditorium
  Thesis director: VAN HULST, NIEK | BOLZONELLO, LUCA
  Thesis abstract: Stimulating an object and watching what the object does in response is the basis of scientific discovery. As much is true for much of experimental nanophotonics, where a material is stimulated with light and we observe how the material responds with nanometric precision. In this thesis I describe the development of a new approach to experimental nanophotonics: Action Microspectroscopy.The exploration of how light and matter interact holds the key to understanding, then harnessing, the properties of matter. For instance, light harvesting materials require a deep understanding of their interaction with energy in order to engineer an optimal combination of light absorption and the ensuing conversion to charge. The syntheses of new optoelectronic materials with exotic properties need precision techniques to observe such properties in action and the unexpected consequences such properties may have. As the leading edge of technology delves deeper into the nanoscale, approaches to explore matter on the same scale must be devised to keep pace.Action Microspectroscopy is a Fourier transform excitation spectroscopy platform designed to energetically, spatially and temporally diagnose the excited state in atomic systems. Its development came in stages, each benchmarked by a chapter in this thesis. Firstly, I demonstrated that the spectral response of many single molecules in a widefield image could be simultaneously acquired, meaning that spatial and spectral detail could be combined to diffractionlimited precision.The second step was to focus on the outcome of the excited state in a two dimensional semiconductor, WSe2. By studying the material’s response in fluorescence and photocurrent, it was possible to determine which exciton (excited state electrons bound to positive holes) states were more likely to lead to charge conversion and which were more likely to re-release their energy as a photon.Finally, by combining spatial detail with temporal resolution in photocurrent detection, I show that the measurement of the exciton-specific transfer of energy in materials can be achieved. I obtain spatially resolved pump-probe measurements of exciton states in WSe2, with a view to spatially resolved 2 dimensional electron spectroscopy.
  

DOCTORAL DEGREE IN POLYMERS AND BIOPOLYMERS

• MUÑOZ GALAN, HELENA: Sensor design and development for autonomous devices for disease diagnosis and therapy
  
  Author: MUÑOZ GALAN, HELENA
  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 POLYMERS AND BIOPOLYMERS
  Department: Department of Chemical Engineering (EQ)
  Mode: Normal
  Deposit date: 22/01/2026
  Reading date: pending
  Reading time: pending
  Reading place: pending
  Thesis director: ALEMAN LLANSO, CARLOS ENRIQUE | PÉREZ MADRIGAL, MARIA DEL MAR
  Thesis abstract: This doctoral research addresses key challenges in diabetes management by integrating sustainable materials, non-invasive sensing, and advanced insulin delivery technologies into a unified framework. Diabetes mellitus is a chronic metabolic disorder that requires continuous glucose monitoring and precise insulin administration to maintain glycemic control and reduce complications. Responding to these needs, the thesis is structured around three major contributions.First, the work advances the development of a previously created non-invasive glucose-monitoring device by incorporating recycled low-density polyethylene (LDPE) into the sensor design. The use of recycled LDPE improves sustainability, cost-efficiency, and environmental compatibility while maintaining reliable sensing performance. This demonstrates the feasibility of repurposing plastic waste in biomedical technologies without compromising functionality.Second, the research explores new strategies for controlled insulin delivery through stimuli-responsive hydrogels. Hydrogels based on poly(γ-glutamic acid) and multi-armed polyethylene glycol (PEG) were engineered to achieve sustained and tunable insulin release. The incorporation of poly(3,4-ethylenedioxythiophene) (PEDOT), a biocompatible conducting polymer, enables electrically triggered and on-demand insulin delivery. This approach offers a minimally invasive alternative to conventional injection-based therapies and highlights the potential of electro-responsive materials in smart drug-delivery systems.Finally, the thesis investigates the nanomechanical properties of multi-armed PEG hydrogels using a microcantilever-based optomechanical sensor. This analysis provides essential insights into the structural behavior, stability, and long-term performance of the hydrogels, contributing to the optimization of their mechanical robustness and responsiveness.Together, these contributions form a comprehensive strategy for improving diabetes care. By integrating non-invasive monitoring, environmentally conscious materials, and intelligent delivery platforms, the research promotes more sustainable biomedical solutions while advancing patient-centered therapeutic tools.
  
• PUERTAS SEGURA, ANTONIO JESUS: Nano-enabled hydrogel coating for prevention of catheter-associated urinary tract infections
  
  Author: PUERTAS SEGURA, ANTONIO JESUS
  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 POLYMERS AND BIOPOLYMERS
  Department: Department of Chemical Engineering (EQ)
  Mode: Article-based thesis
  Deposit date: 16/12/2025
  Reading date: 06/03/2026
  Reading time: 15:00
  Reading place: UPC-ESEIATTSala de conferències - TR5C. Colom, 1 - TERRASSAhttps://utgct.upc.edu/ca/imatges/espais-i-recursos/zones-destudi/espai-tr5
  Thesis director: TZANOV, TZANKO KALOYANOV | CIARDELLI, GIANLUCA
  Thesis abstract: Catheter-associated urinary tract infections (CAUTIs) represent a critical healthcare challenge, accounting for a substantial proportion of the nosocomial infections worldwide and imposing significant economic burdens on healthcare systems through prolonged hospitalisation, additional treatments, and increased healthcare costs. These infections are primarily initiated by bacterial adhesion to catheter surfaces, followed by the formation of structured biofilms that protect pathogens against host immune defences and antimicrobial treatments. Biofilm-embedded bacteria exhibit significantly enhanced antibiotic tolerance and facilitate horizontal gene transfer, thereby accelerating the emergence of multidrug-resistant strains. Current clinical strategies, including catheter replacement, systemic antimicrobial therapy, and conventional surface coatings, often prove inadequate due to limited efficacy duration, poor selectivity, or cytotoxicity concerns. This underscores the urgent need for innovative, multifunctional, and biocompatible solutions capable of preventing biofilm establishment whilst maintaining excellent biological compatibility.The present doctoral thesis addresses these challenges through the design and development of advanced nano-enabled hydrogel-based coatings, specifically engineered to enhance the performance of urinary catheters. Innovative coatings were engineered to incorporate diverse antibacterial and antibiofilm nanomaterials, including lauryl gallate-, silver-, ceragenin-, and lignin-based nanoparticles, employing green and cost-effective methodologies such as sonochemical deposition and enzymatic grafting. These nano-actives were incorporated in bio-based and antifouling polymers including chitosan, catechol-modified gelatine, and polyzwitterions. The resulting hybrid coatings were engineered to combine synergistic contact-killing and sustained-release antimicrobial mechanisms with enhanced surface hydration and superior resistance to bacterial adhesion.Comprehensive physicochemical characterisation confirmed the successful integration of the nano-enabled coatings onto indwelling urinary catheters, revealing tailored surface morphology, high stability, and controlled release profiles of the active compound. In vitro assays demonstrated potent bactericidal activity and biofilm inhibition against clinically relevant uropathogens, including Escherichia coli, Pseudomonas aeruginosa, and Staphylococcus aureus, under both static and dynamic flow conditions that simulate physiological environments. Cytotoxicity studies revealed high biocompatibility with human fibroblasts and keratinocytes, confirming safety for prolonged medical applications. Importantly, in vivo experiments using a rabbit catheterisation model showed significant reductions in microbial colonisation and excellent biocompatibility in animals fitted with the coated catheters, validating the protective performance of these devices under realistic physiological conditions.This thesis establishes a framework for the design and implementation of nano-enabled coatings that synergistically combine antimicrobial efficacy, biofilm resistance, and host compatibility. The findings present promising pathways for advancing next-generation urinary catheter technologies and provide a solid foundation for clinical translation, ultimately aiming to minimise CAUTI incidence and reduce the global burden of antimicrobial resistance.
  

DOCTORAL DEGREE IN SIGNAL THEORY AND COMMUNICATIONS

• PÉREZ GUIJARRO, JORDI: On Quantum Supervised Learning and Learning Techniques for Quantum Error Mitigation
  
  Author: PÉREZ GUIJARRO, JORDI
  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 SIGNAL THEORY AND COMMUNICATIONS
  Department: Department of Signal Theory and Communications (TSC)
  Mode: Normal
  Deposit date: 22/12/2025
  Reading date: 13/02/2026
  Reading time: 11:00
  Reading place: Aula D5-007, Edifici D5, Campus Nord UPC, Barcelona
  Thesis director: RODRIGUEZ FONOLLOSA, JAVIER | PAGES ZAMORA, ALBA MARIA
  Thesis abstract: The development of quantum computers promises to drastically reduce the time required to solve certain computational problems. Among their most promising applications is the field of machine learning. However, significant uncertainty remains in this area. In particular, it is still unclear under which learning scenarios quantum algorithms will outperform their classical counterparts. This thesis aims to deepen our understanding of when quantum speed-ups can be expected in machine learning tasks. Specifically, we examine the connection between learning speed-ups and the more extensively studied phenomenon of quantum computational speed-up. We conclude that, in cases where the training set can be classically generated, the two are equivalent concepts, and we provide examples of such functions based on the prime factorization problem.Importantly, quantum machine learning is not only concerned with improving classical learning algorithms using quantum computation but also with learning from quantum data. In this context, we investigate a learning scenario in which the inputs to the target functions are quantum states, thereby generalizing the classical supervised learning framework. To this end, we first focus on the problem of quantum hypothesis testing, which can serve as a subroutine for both the problems of evaluating a function and learning a function. Specifically, we derive several sequential methods for solving the problem of quantum hypothesis testing, along with a lower bound on the resources required. This lower bound immediately implies corresponding lower bounds for the problems of learning and evaluating functions. Additionally, we develop a learning method based on the classical shadows technique.Finally, after exploring how quantum processes can aid learning tasks, we examine how classical learning techniques can, in turn, enhance quantum computing. In particular, we study how classical machine learning methods can be used to mitigate the effects of noise in quantum devices, with a focus on quantum error mitigation. Specifically, novel feature maps are proposed for the technique known as Clifford data regression. First, a theoretical justification for these feature maps is provided, followed by an analysis and a subsequent evaluation of their performance through numerical experiments. It is concluded that, for some of the proposed feature maps, a performance improvement is indeed achieved.
  

DOCTORAL DEGREE IN STATISTICS AND OPERATIONS RESEARCH

• BARRERA GÓMEZ, JOSE ANTONIO: Extension of statistical methods for time series analysis with applications in environmental epidemiology
  
  Author: BARRERA GÓMEZ, JOSE ANTONIO
  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 STATISTICS AND OPERATIONS RESEARCH
  Department: Department of Statistics and Operations Research (EIO)
  Mode: Normal
  Deposit date: 23/12/2025
  Reading date: 25/03/2026
  Reading time: 11:00
  Reading place: Sala d'actes de la FMECampus Diagonal Sud, Edifici U. C. Pau Gargallo, 14 08028 Barcelona
  Thesis director: BASAGAÑA FLORES, XAVIER | GINEBRA MOLINS, JOSEP
  Thesis abstract: Part of research in environmental epidemiology focuses on the assessment of associations betweenthe exposure to environmental factors and health outcomes based on aggregated longitudinal data in a given population. To model such associations, time series analysis is typically used, in which informational units are time points (e.g. days or weeks). An aggregated measure of the outcome at each time point (e.g. yearly mean cognitive test score or daily mortality count) is linked to an aggregated measure of exposure to some environmental factor of interest at the same time point (e.g. yearly mean air pollution level or daily mean ambient temperature) using a suitable regression (e.g. linear or Poisson) model. In this context, this thesis develops two studies.The first study deals with collinearity. Distributed lag models (DLNMs) have been increasingly used to model delayed effects of environmental factors on health. DLNMs include as predictors the same exposure measured at different time points. Those lagged variables are often highly correlated resulting in correlations between the estimated regression coefficients corresponding to different lags, which can lead to unreliable results. We first illustrate such problems and then propose a visual diagnosis tool to assess consequences of such collinearity. Essentially, new values of the outcome are simulated under an alternative hypothetical effect of the exposure of interest. Then, the original model is fitted again but now using the simulated data. Finally, both original and new results are compared graphically to assess if unexpected results obtained in the original analysis could be driven by collinearity. The tool is implemented in the R package collin. We provide illustrative examples and a user’s guide.The second study extends the Poisson regression model in multi-zone time series analysis for a count outcome. Those models need to control for trends and seasonality, which can be done by including time-stratum indicators (e.g. unique combinations of year, month and day of week). That implies having to include in the model a typically high number of nuisance parameters that can cause computational issues in the estimation process. This problem can be avoided with the conditional Poisson regression model, by conditioning by the sum of the outcome event counts in each stratum, which results in a multinomial regression model. By doing this, the nuisance parameters do not need to be estimated while the model provides relative risks (e.g. change in the mean mortality for a given increase in air pollution concentration) that are adjusted for long-term trends and seasonality. In cases of data from different geographical zones, a two-stage modelling procedure is usually performed, first analysing each zone separately and then combining zone-specific results into a single overall measure using, for instance, meta-analysis. A one-stage analysis, by analysing simultaneously data from all geographic zones, could be performed by including a random effect at zone level. However, the available (frequentist) software for conditional Poisson regression does not allow including random effects. In this context, we propose and develop a one-stage modelling approach, which is computationally feasible, namely Bayesian conditional Poisson mixed model, to analyse time series data for a count outcome that analyses all zones simultaneously while maintaining the good properties of the two-stage analysis. Our approach is based on conditioning out by the sum of the outcome event counts in each zone-time stratum and the inclusion of a random effect to model zone-specific association of interest. In addition, our method allows for including a spatial structure of the random effects as well as considering potential overdispersion. In the study, we derive model equations and implement the modelling procedure in R. To facilitate usage, we develop illustrative examples and provide code and data.
  
• BORJA ROBALINO, RICARDO STALIN: Optimización bayesiana en técnicas machine Learning clásicas: redes neuronales y XGBoost y su aplicación como modelos predictores de diabetes en pacientes ecuatorianos
  
  Author: BORJA ROBALINO, RICARDO STALIN
  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 STATISTICS AND OPERATIONS RESEARCH
  Department: Department of Statistics and Operations Research (EIO)
  Mode: Normal
  Deposit date: 22/01/2026
  Reading date: pending
  Reading time: pending
  Reading place: pending
  Thesis director: MONLEON GETINO, ANTONIO | GIBERT OLIVERAS, CARINA
  Thesis abstract: Machine learning (ML) is a branch of artificial intelligence that allows human capabilities to be imitated through various algorithms and techniques that learn from data using learning processes (supervised, unsupervised, or reinforcement) for decision-making with minimal human intervention. Classic ML models have generated great results in the automation of classification and regression processes in various areas. Within classification, artificial neural networks (ANN) have gained relevance due to their ability to learn and model complex nonlinear relationships. Similarly, the XGBoost model based on decision trees has demonstrated great efficiency, speed, scalability, and performance, winning several competitions. On the other hand, Bayesian inference has provided a probabilistic and revolutionary framework for optimizing machine learning models, with the implementation of uncertainty in the estimation process, combining evidence with prior beliefs, in order to reduce overfitting and improve predictions by adjusting parameters and hyperparameters.This research aims to optimize two classic machine learning techniques (artificial neural networks and XGBoost) for classification using Bayesian inference and to build a diabetes prevention model for the Ecuadorian population. The study begins with a theoretical and mathematical conceptualization of each algorithm, followed by an analysis of the various points of intervention, programming, and implementation of Bayesian models using Markov chain Monte Carlo (MCMC) estimation techniques and variational inference (VI), validation using public databases, implementation of a client-server system with multiple specialized backends, and, finally, the development of a real application as predictors of type 1, type 2, and gestational diabetes.As a result, a Bayesian model was implemented in artificial neural networks (ANN) at two inference points. The first adjusted the parameters at each backpropagation step; however, it presented itself as an option with a prohibitive computational overhead. As a second intervention, an adjustment was made to the activation function in the final layer, obtaining positive and computationally viable results. In the case of XGBoost, the predictions were adjusted at each boosting step before vectorization, demonstrating high predictive power in both the use of the MCMC technique and IV. Validation with the Pima Indians Diabetes database and the use of various distribution functions demonstrated the robustness and sensitivity of the implemented models, while generalization and consistency were verified through application to various databases. In all cases, results superior to or equal to those obtained using the traditional model were obtained, depending on the characteristics of the data.In addition, a web application (client-server) was implemented with Bayesian proposals, allowing users to interact with the models in an easy and intuitive way, with options for data loading, parameter configuration and probability distributions, estimation techniques (MCMC or IV), training-validation process or use of cross-validation, real-time results, and model download options. The application of the Bayesian proposal to a real case, such as the prediction of type 1, type 2, and gestational diabetes, with data from Ecuadorian patients, presented encouraging results (accuracy = 99.47%), becoming the first predictive model for the three types of diabetes at the regional and national level, confirming that the use of this approach is an excellent alternative for the optimization of machine learning models.
  

DOCTORAL DEGREE IN STRUCTURAL ANALYSIS

• ALCAYDE ROMO, BARBARA: Numerical modelling of the fatigue behaviour of composites. Application to the automotive industry.
  
  Author: ALCAYDE ROMO, BARBARA
  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 STRUCTURAL ANALYSIS
  Department: Department of Civil and Environmental Engineering (DECA)
  Mode: Normal
  Deposit date: 19/12/2025
  Reading date: 06/03/2026
  Reading time: 11:00
  Reading place: Sala Zienkiewich (CIMNE) Building C1, UPC - Campus North Gran Capitan S/N 08034 Barcelona
  Thesis director: BARBU, LUCIA GRATIELA | CORNEJO VELÁZQUEZ, ALEJANDRO
  Thesis abstract: In an engineering landscape increasingly focused on optimized design, lightweight materials, and multifunctional performance, accurately predicting the fatigue behaviour of composite materials under realistic service conditions is essential. Traditional approaches to fatigue analysis in Fibre Reinforced Polymers (FRP) often rely on simplified extrapolations of laboratory data or homogenized models that neglect the complex interactions between constituent materials and environmental influences. Moreover, these approaches typically fail to account for temperature variations. Such reductionist perspectives limit the ability to capture the coupled mechanical and thermal degradation mechanisms inherent to advanced materials. This thesis proposes a unified numerical framework grounded in the Finite Element Method (FEM), integrating a phenomenological homogenization strategy, the Serial Parallel Rule of Mixtures Law (SP-RoM), with a High Cycle Fatigue (HCF) Constituive Law (CL). This approach enables the simultaneous representation of the distinct fatigue responses of fibres and matrix within layered composite laminates, accounting for variations in stacking sequence and fibre orientation. A key innovation is a calibration methodology that infers fatigue parameters at constituent level from experimental data at laminate scale, thus overcoming the challenges of direct testing of individual components. Furthermore, the work presents a thermomechanically coupled fatigue model incorporating temperature dependent material properties and thermal expansion, generalizing classical fatigue life prediction curves to fluctuating and spatially varying temperature fields. To address the significant computational demands of fatigue simulations, an Advance in Time Strategy (AITS) Cycle Jump (CJ) is developed, enabling efficient simulation of long-term fatigue damage evolution without sacrificing accuracy. Validated against experimental benchmarks and literature data, the proposed methodology advances fatigue life prediction in composite materials by delivering a flexible, robust, and computationally efficient tool. Additionally, the fatigue formulation has been enhanced to capture complex thermomechanical effects. This work lays the foundation for future research on integrated modelling of fatigue and multiphysics deterioration phenomena in advanced composite structures.
  

DOCTORAL DEGREE IN SUSTAINABILITY

• VALLEJOS CARTES, ROSANA: Examinando preferencias, motivaciones y actitudes de los consumidores para mejorar la sostenibilidad de los sistemas agroalimentarios. Una aplicación a los sistemas agropastorales extensivos.
  
  Author: VALLEJOS CARTES, ROSANA
  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 SUSTAINABILITY
  Department: University Research Institute for Sustainability Science and Technology (IS.UPC)
  Mode: Normal
  Deposit date: 22/12/2025
  Reading date: 20/03/2026
  Reading time: 12:30
  Reading place: Salón de grados de la EEABB en Castelldefels
  Thesis director: GIL ROIG, JOSE MARIA
  Thesis abstract: In the context of global transformations in the agri-food system—marked by environmental pressures, biodiversity loss, market concentration, and dietary homogenization—a critical scenario for sustainability has emerged. Within this landscape, extensive livestock farming, based on grazing and traditional management practices, constitutes a viable alternative to intensive production by integrating ecological, economic, and sociocultural dimensions. Nevertheless, its long-term viability depends largely on consumer recognition, as purchasing decisions can drive the transition toward more sustainable systems. This thesis assumes that consumers operate as agents of change but face constraints linked to limited information, perceptions of high prices, and insufficient market differentiation. Accordingly, the general objective was to assess the economic and social viability of extensive livestock systems through an examination of consumer preferences, motivations, and attitudes toward sustainably produced lamb meat, generating evidence to inform differentiation strategies and public policies that support agri-food sustainability. The research employed a mixed-methods design developed in two complementary phases. The qualitative phase comprised semi-structured interviews and participatory workshops with sheep producers to identify sustainability drivers and market-valued attributes. The quantitative phase implemented Discrete Choice Experiments (DCEs) among consumers in Catalonia, carried out in two stages: an exploratory study (n = 396) to refine attributes and optimize the experimental design, followed by a larger study (n = 1,003) incorporating attitudinal measures through the New Ecological Paradigm (NEP) scale. Data were analyzed using mixed logit and latent class models, enabling the identification of heterogeneous preferences, the estimation of willingness to pay, and the integration of environmental and ethical attitudes into consumer choice modeling.The findings reveal a complex interaction between knowledge, attitudes, and values in shaping preferences for sustainable meat. Results indicate limited consumer knowledge of production systems—particularly extensive livestock farming—despite the strong symbolic valuation of local origin, animal welfare, and territorial authenticity. Labeling and certification mechanisms emerge as essential tools for building trust and supporting informed decisions. The highest willingness to pay is associated with organic production and animal welfare, while sensory cues such as color and visible fat exert a complementary influence.The research confirms a persistent gap between stated attitudes and actual behaviors, shaped by economic constraints, purchasing routines, and information availability. However, it also identifies a segment of consumers who are informed and value-consistent, suggesting opportunities for differentiation through targeted communication, certification, and education initiatives. Overall, the thesis provides an integrated understanding of the relationship between consumer behavior and sustainability, advocating for a renewed appreciation of agro-pastoral systems as public goods that deliver not only food but also essential ecosystem and cultural services. Its results contribute to the development of agri-food policies and market strategies that acknowledge the role of consumer demand in advancing sustainable production models, reinforcing consumers as central actors in the transition toward an ethical, territorially grounded, and environmentally responsible agri-food economy.
  

DOCTORAL DEGREE IN URBAN AND ARCHITECTURAL MANAGEMENT AND VALUATION

• SIFUENTES MUÑOZ, BLANCA CAROLINA: Transformación urbana y movilidad sostenible: construyendo una Barcelona car-free
  
  Author: SIFUENTES MUÑOZ, BLANCA CAROLINA
  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 URBAN AND ARCHITECTURAL MANAGEMENT AND VALUATION
  Department: Department of Architectural Technology (TA)
  Mode: Normal
  Deposit date: 15/12/2025
  Reading date: 02/03/2026
  Reading time: 16:30
  Reading place: ETSAB - Pl. Baja - Sala GradosAv. Diagonal, 649-BCN(Enlace a videoconf: https://meet.google.com/uko-xgpp-byk - Inicio conexión: 16:00 hora Bcn)
  Thesis director: ROCA CLADERA, JOSE NICASIO | ARELLANO RAMOS, BLANCA ESMARAGDA
  Thesis abstract: The configuration of contemporary cities has been deeply shaped by the hegemony of the automobile as a structuring axis of territory, mobility, and public space. This model, consolidated since the mid-20th century, has led to dispersed, exclusionary, and unsustainable urban forms, limiting quality of life and hindering the creation of more equitable and resilient environments. In response, critical approaches have emerged advocating for a paradigm shift toward people-centered cities, the right to the city, and sustainable mobility.In this context, this doctoral thesis aims to construct prospective scenarios for a car-free Barcelona by 2050, through a structural and multiscalar analysis of its mobility system, urban planning, and use of public space. A mixed-methods approach is adopted, integrating six methodological lines: (1) collection and preprocessing of mobility data from the Barcelona Metropolitan Area (AMB); (2) exploratory factor analysis (EFA) on a longitudinal AMB database; (3) trend analysis using regression and ARIMA models to project modal shifts; (4) comparative analysis of Amsterdam and Copenhagen as international car-free transition benchmarks; (5) expert consultation through a disaggregated Delphi method; and (6) construction of contrasted future scenarios.The results identify latent structures in the mobility system, tensions between urban policies and actual mobility practices, and institutional challenges linked to multilevel governance. The developed scenarios outline alternative urban futures, from continuity-based models to deep transformations, highlighting their implications in terms of equity, sustainability, and the right to the city.This research provides an original contribution by integrating approaches from sustainable mobility, prospective planning, and multiscalar analysis. Its findings guide the formulation of public policies and urban strategies toward more just, healthy, and sustainable post-car cities. Ultimately, it proposes conceptual and methodological tools to rethink urbanism through the lens of deep transformation in the face of climate, social, and territorial uncertainty.