DOCTORAL DEGREE IN ARCHITECTURAL, BUILDING CONSTRUCTION AND URBANISM TECHNOLOGY

• GONZÁLEZ ESPINOSA, VANESSA: Diseño de materiales cementícios reforzados con fibras vegetales impregnadas con materiales de cambio de fase para mejorar el comportamiento térmico de las cubiertas de los edificios.
  
  Author: GONZÁLEZ ESPINOSA, VANESSA
  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 ARCHITECTURAL, BUILDING CONSTRUCTION AND URBANISM TECHNOLOGY
  Department: Department of Architectural Technology (TA)
  Mode: Normal
  Deposit date: 31/07/2025
  Reading date: 28/11/2025
  Reading time: 11:00
  Reading place: ETSAB (Escuela Técnica Superior de Arquitectura de Barcelona) - Planta Baja - Sala de GradosAv. Diagonal, 649-651 - 08028 - Barcelona
  Thesis director: CLARAMUNT BLANES, JOSE | LACASTA PALACIO, ANA MARIA
  Thesis abstract: In the current context, sustainable construction prioritises innovative materials that combine energy efficiency, mechanical strength and safety against extreme conditions, such as fire, in order to address the challenges of climate change and human needs. Fibre-reinforced cementitious composites and phase change materials (PCM) are emerging as a promising solution, particularly in raised roof pavements, where thermal regulation is essential. The main objective of this thesis is to develop a cement board reinforced with non-woven vegetable fibres and PCM for raised roof pavements, determining the optimal dosage of cement, fibres and PCM that maximises mechanical resistance and thermal regulation capacity, as well as analysing its response to fire. The research seeks to advance the integration of PCM and plant fibres into cementitious matrices, proposing improvements for their practical application in construction with an environmental focus.The methodology, which is highly experimental in nature, was structured in several stages. First, the state of the art on cementitious composites and PCM was reviewed, identifying trends and challenges. Subsequently, an experimental campaign was designed that included: selection of materials (commercial cements, non-woven fibres and pure and microencapsulated PCMs), formulation of mixtures with different proportions of fibres and PCMs compared to a control without PCMs, evaluation of mechanical properties (flexural strength), thermal properties (conductivity, thermal storage and retardation) and fire behaviour through standardised tests, and statistical analysis to determine the impact of each component.The results show that the incorporation of PCM RT28 through direct impregnation into non-woven fibres in cementitious composites improves thermal properties, achieving a delay in temperature changes comparable to that of microencapsulated PCM mixed into the cementitious matrix. However, the composite made with PCM RT28 has superior mechanical strength, with a modulus of rupture (MOR) approximately three times greater than that of the microencapsulated composite, although both show a decrease in strength compared to samples without PCM. The non-woven fibres, by effectively impregnating the PCM, reinforce the cohesion of the composite and preserve hardening by deformation, partially mitigating the loss of mechanical strength.Although both the vegetal fibres and the PCM used are organic in nature and therefore combustible, the cementitious composites exhibited good fire performance, with low-intensity flames and a high self-extinguishing capacity once the heat source was removed. Overall, the samples with PCM incorporated through fibre impregnation showed better fire behaviour than those formulated with microencapsulated PCM: although ignition occurred slightly earlier, the total heat released (THR), as measured in the cone calorimeter tests, was substantially lower.This combination, which has been little explored, balances thermal efficiency and structural functionality, with direct applications in sustainable buildings. The research provides a detailed analysis of the interaction between plant fibres, PCM and the cement matrix, proposing optimal dosages and strategies to mitigate fire-related risks. The results lay the foundations for future research and practical applications, promoting the development of more efficient and sustainable building materials.
  

DOCTORAL DEGREE IN AUTOMATIC CONTROL, ROBOTICS AND VISION

• DALMASSO BLANCH, MARC: Cooperative Planning and Negotiation in Human-Robot Teams
  
  Author: DALMASSO BLANCH, MARC
  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: Institute of Robotics and Industrial Informatics (IRI)
  Mode: Normal
  Deposit date: 01/10/2025
  Reading date: 10/11/2025
  Reading time: 11:00
  Reading place: Sala d'Actes de la Facultat de Matemàtiques i Estadística (FME), Campus Diagonal Sud, Edifici U. C. Pau Gargallo, 14, 08028 Barcelona
  Thesis director: SANFELIU CORTES, ALBERTO
  Thesis abstract: As robots become increasingly integrated into everyday environments, rigid role paradigms and unilateral control models fall short of enabling meaningful collaboration. Preserving human autonomy while allowing robots to contribute proactively in shared decision-making tasks introduces the need for alignment and negotiation between agents. Negotiation arises not merely as a design preference but as a requirement when autonomous entities with partial knowledge, differing capabilities, or misaligned goals must act jointly in real-world settings.This thesis investigates the challenge of integrating robots into human teams in unstructured environments, with a particular focus on Human-Robot Collaborative Navigation (HRCN). It seeks to empower them as active decision-making agents who flexibly and critically adapt to human preferences and needs. This technological development is framed as a social necessity: without it, robots would remain confined to controlled environments, or people would lose agency by having to adapt to rigid robot behaviour.The core contributions of the thesis are threefold. First, it introduces the Social Reward Sources (SRS) model, a shared spatial and task representation for Human-Robot Teams (HRT). Second, it presents a multi-agent planning system leveraging the SRS model to generate collaborative plans for heterogeneous teams. Third, it proposes a negotiation framework for Human-Robot Plan Negotiation (HRPN), incorporating a novel plan characterisation model, the cooperativeness space. These and additional secondary contributions are validated through real-world experiments within the collaborative object search benchmark.Altogether, the thesis offers a pathway for deploying robots as collaborative agents capable of negotiation, thereby supporting agency-preserving human-robot interaction in open-world contexts.
  
• DELGADO GUERRERO, JUAN ANTONIO: Learning latent structures for robotic assistance in daily manipulation tasks
  
  Author: DELGADO GUERRERO, JUAN 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 AUTOMATIC CONTROL, ROBOTICS AND VISION
  Department: Institute of Robotics and Industrial Informatics (IRI)
  Mode: Normal
  Deposit date: 02/09/2025
  Reading date: 27/11/2025
  Reading time: 11:00
  Reading place: Sala de Juntes de la Facultat de Matemàtiques i Estadística (FME), Campus Diagonal Sud, Edifici U. C. Pau Gargallo, 14, 08028 Barcelona
  Thesis director: TORRAS GENIS, CARMEN | COLOMÉ FIGUERAS, ADRIÀ
  Thesis abstract: Robotic domestic assistance presents significant challenges due to the complexity of modeling everyday manipulation tasks, especially those involving deformable objects like cloth. Traditional approaches often struggle with high-dimensional state representations, dynamic uncertainties, and the need for safe human-robot interaction. This thesis addresses these challenges by developing novel machine learning methods based on latent variable models to enable efficient, adaptive, and safe robotic manipulation.First, we propose a Gaussian Process Latent Variable Model (GPLVM) framework combined with Bayesian Optimization (BO) to learn high-dimensional robot motion policies with minimal data. This approach reduces the parameter space dimensionality while preserving task-relevant features, achieving faster convergence than other existing model-free alternatives.Next, we extend this framework to contextual learning using Covariate GPLVM (c-GPLVM), allowing robots to adapt to environmental changes (e.g., user preferences, object positions) without retraining. Experiments in feeding and shoe-fitting tasks demonstrate improved generalization with fewer samples compared to state-of-the-art contextual policy search methods.For dynamic cloth manipulation, we introduce the Controlled Gaussian Process Dynamical Model (CGPDM), which embeds control actions into a low-dimensional latent space to predict cloth motion under robot manipulation. Evaluations in simulated and real-world bimanual cloth handling show that CGPDM accurately generalizes to unseen actions, even with limited training data.Finally, we address safety in human-robot interaction by proposing Cartesian control enhancements for redundant manipulators, including error saturation, singularity avoidance, and impedance tuning. These measures mitigate risks during physical interaction, ensuring stable and compliant robot behavior.Together, these contributions advance robotic cloth manipulation by combining data-efficient learning, context-aware adaptation, and safe control, paving the way for practical deployment in assistive and household robotics.
  
• GARCIA CAMACHO, IRENE: Benchmarking cloth manipulation
  
  Author: GARCIA CAMACHO, IRENE
  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: Institute of Robotics and Industrial Informatics (IRI)
  Mode: Normal
  Deposit date: 25/09/2025
  Reading date: 24/11/2025
  Reading time: 11:00
  Reading place: Aula Capella, Escola Tècnica Superior d'Enginyeria Industrial de Barcelona (ETSEIB), Edifici PI (Pavelló I). Av. Diagonal, 647, Barcelona
  Thesis director: ALENYÀ RIBAS, GUILLEM | BORRÀS SOL, JÚLIA
  Thesis abstract: Benchmarking is a crucial tool in research for fostering progress in a field. It consists on standardized frameworks for evaluation, quantifying the performance of an approach in comparison to previous works to determine the improvements and progress made. Robotic cloth manipulation presents many challenges due to the high deformable nature of textile objects. It is an interdisciplinary field that integrates many components such as control, perception and hardware, among others, to solve cloth manipulation problems with a wide variety of robotic platforms, end-effectors, objects and strategies. This variability makes difficult to design general evaluation procedures that can be adopted by the vast researchers in the field. This thesis addresses the need for standardized benchmarks in cloth manipulation, providing solutions for the key aspects of which a benchmark is composed: setup description, task description and evaluation procedures. The thesis starts with the design of benchmarks for relevant cloth manipulation tasks, proposing clear procedures and metrics to assess the performance quality. Setup standardization is improved with the introduction and distribution of a standardized cloth object set with textile household objects, given that object directly impact on the manipulations required and results obtained. Additionally, we propose a framework to characterize textile objects through its physical and mechanical properties, so we can deal with object's wear and usage over time, maintaining the standardization and extending it to textile objects of other categories. Subsequently, we proposed a scene state definition of cloth manipulation based on its configuration, grasp type and grasp location to represent cloth manipulation tasks and build more informed evaluation metrics. Later delving into state estimation for decision-making and benchmarking. The thesis culminates with the organization of a cloth manipulation and perception competition, done for joining research groups in comparing their systems with equal test conditions and raise awareness of the importance of designing and adopting standardized evaluation processes to foster in the field.In summary, this thesis touches on benchmarking, standardization, task representation, and decision-making in the context of cloth manipulation.
  
• IZQUIERDO BADIOLA, SILVIA: Hybrid Systems for Human-Centered Robotics: Combining Symbolic and Generative AI for Flexible and Adaptive Plan Generation and Execution
  
  Author: IZQUIERDO BADIOLA, SILVIA
  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: Institute of Robotics and Industrial Informatics (IRI)
  Mode: Normal
  Deposit date: 29/09/2025
  Reading date: 18/11/2025
  Reading time: 10:00
  Reading place: Sala d'Actes de la Facultat de Matemàtiques i Estadística (FME), C/ Pau Gargallo, 14, 08028 Barcelona
  Thesis director: ALENYÀ RIBAS, GUILLEM | RIZZO, CARLOS ERNESTO
  Thesis abstract: Robots are rapidly leaving structured factory floors and entering human-rich environments such as homes, hospitals, and shared workplaces. A human-centric approach to developing robot behavior is essential for effective collaboration and acceptance of robots in such dynamic settings. This entails enabling robots to generate plans that continuously adapt to the evolving environment and human states, proactively preventing failures, while allowing those plans and models to be specified in a flexible, human-intuitive manner. This thesis contributes toward this goal through an approach driven by two complementary strategies: (i) foundational, structured planning and agent-modeling techniques, and (ii) their extension with Large Language Model (LLM) capabilities, resulting in hybrid systems capable of more general and adaptive behavior.We develop four main contributions, each targeting a specific challenge. First, to address the lack of effective integration of human states in Human-Robot Collaboration (HRC) planning, often resulting in failures, we propose a framework that incorporates an agent model into task planning via action cost modulation, targeting proactive failure prevention. Second, to tackle the challenge of estimating agent-specific action costs in data-scarce HRC scenarios, we introduce a simulation-based learning framework. Third, to overcome the rigidity and modeling effort of current systems, we develop a planning framework that translates natural language human goals and agent conditions into structured planning problems, enabling more flexible and intuitive plan generation. Fourth, recognizing that plan execution may encounter issues not foreseeable at planning time, we present an agent for context-aware issue detection, explanation, and recovery, leveraging a regulated interaction between an LLM and grounded perception and interaction tools. Collectively, these contributions, supported by published results, address three core objectives: (O1) integrating task planning with agent modeling to produce human-adaptive plans; (O2) devising flexible techniques for defining planning, action, and agent models; and (O3) implementing failure-prevention mechanisms for dynamic, human-centric environments.This thesis embraces the shift from rigid, task-specific systems toward adaptive, generalizable robotics by combining structured symbolic methods with generative AI. Key challenges for this transition are identified, and targeted solutions are proposed to inform and guide future advancements in human-centered robotics. Through hybrid approaches, this research advances flexible, natural plan generation that adapts to human preferences and states while proactively preventing failures during execution, laying the groundwork for a future unified system capable of real-world, human-aware adaptability.
  
• PUERTO SANTANA, CRISTIAN: Design, implementation, and evaluation of novel fault detection methodologies for time varying industrial mechanical systems
  
  Author: PUERTO SANTANA, CRISTIAN
  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: 25/09/2025
  Reading date: 28/11/2025
  Reading time: 11:00
  Reading place: Aula 28.8, Escola Tècnica Superior d'Enginyeria Industrial de Barcelona (ETSEIB), Avinguda Diagonal, 647, 08028 Barcelona
  Thesis director: OCAMPO MARTINEZ, CARLOS AUGUSTO | DÍAZ ROZO, JAVIER
  Thesis abstract: This dissertation addresses the necesity of scalable and robust fault detection methods in industrial mechanical systems operating under complex, noisy, and variable conditions. These systems—comprising components such as rotors, gears, and structural frameworks—play a vital role in industrial operations, and their failures can lead to substantial performance degradation, safety risks, and increased maintenance costs. Motivated by these challenges, the research proposes data-driven methodologies designed to improve the accuracy, reliability, and scalability of condition monitoring in these environments. Unlike traditional model-based methods that rely heavily on system-specific knowledge and simulation, the approaches developed in this work emphasize adaptability, minimal calibration, and real-world applicability. The thesis begins with a detailed examination of industrial mechanical systems and a critical review of existing fault detection methods, particularly those targeting mechanical unbalance, gear defects, and structural resonance. This foundation highlights the limitations of current technologies and sets the stage for the development of six original contributions.A central innovation in this research is a method for automatically identifying transient and stationary regimes in multivariate systems using statistical tools, signal processing, and geomery of spatial curves. This regime classification is essential for segmenting data accurately and applying appropriate fatult detection strategies. Building on this framework, the thesis introduces two novel condition indicators: one for detecting mechanical unbalance using non-intrusive signal processing techniques, and another for identifying gear faults under fluctuating speed conditions via Gaussian mixture models and the Cauchy-Schwarz divergence. Both indicators are designed to function reliably in noisy environments and are validated on experimental platforms that replicate real industrial conditions. Another major contribution extends monitoring capabilities to both rotordynamic elements and structural components within a single system, demonstrated through a power generation setup. This cross-monitoring approach integrates data-driven modeling to detect anomalies that may result from mechanical faults or structural instabilities, offering a more comprehensive diagnostic tool.In addition to enhancing fault detection in rotating machinery, the dissertation explores advanced techniques in structural health monitoring. Using long-term bridge data, the research applies the Hankel alternative view of Koopman analysis to extract meaningful features and assess parameter sensitivity. This is complemented by a new feature extraction strategy that leverages regime classification to enhance resilience to noise and reduce computational costs. The final contribution is a probabilistic modeling framework based on Copula functions, which allows for flexible, noise-tolerant modeling of feature distributions while incorporating prior knowledge. Collectively, these methodologies form a unified framework capable of operating under real-world industrial constraints. The research not only demonstrates high performance across varied fault types and systems but also advances the field by offering tools that can adapt to multivariate, dynamic, and uncertain conditions without requiring extensive system modeling or manual settings.
  
• VERMA, PARIKSHIT: Control strategies for the traffic management of AGV-based transportation systems
  
  Author: VERMA, PARIKSHIT
  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: Institute of Industrial and Control Engineering (IOC)
  Mode: Normal
  Deposit date: 01/10/2025
  Reading date: 13/11/2025
  Reading time: 17:00
  Reading place: Aula 28.8, Edifici I, Planta 1, Escola Tècnica Superior d'Enginyeria Industrial de Barcelona (ETSEIB), Av. Diagonal 647, 08028, Barcelona
  Thesis director: OLM MIRAS, JOSEP MARIA | SUAREZ FEIJOO, RAUL
  Thesis abstract: Current research in fleet control of Automatic Guided Vehicles (AGVs) focuses on enhancing their efficiency and integration within industrial environments like manufacturing plants and warehouses. The AGVs are primarily used for material transportation and, when effectively integrated into factory workflows, offer significant advantages in terms of flexibility and scalability. This integration enables factories to dynamically distribute the work to processing stations and expand the system by adding new AGVs or workstations with minimal disruption. A major challenge lies in managing diverse AGV under a unified control system to maintain smooth operations as transportation demands vary. One core aspect of managing AGV fleets is traffic control in shared environments. Typically, these indoor spaces are pre-mapped, and all the AGVs share access to this map, which includes road networks and Points of Interest (POIs) like pick-up/drop-off zones, charging stations, and parking areas. Since these POIs can only be occupied by one AGV at a time, traffic management systems must address which AGV occupies which point, identify potential conflicts, schedule AGV movement out of parking zones, and decide which AGVs get priority at shared points. Another vital component is task allocation, i.e. determining which AGV should perform a specific task and when. This decision depends on multiple factors, including the AGV’s current location, battery status, load capacity, traffic conditions, and the urgency of the task. Efficient task allocation and traffic control are deeply interconnected; managing one often involves considerations of the other. For instance, by deciding the optimal timing of task execution, the system can reduce conflicts and enhance overall performance. While existing fleet management approaches address many of these issues, there remains considerable room for improving coordination, adaptability, and optimization in AGV-based transportation systems.This thesis explores various features of a multi-AGV-based transportation system, providing a comprehensive overview of its operational aspects. However, the primary contributions of this work are centered on three key areas. Firstly, it presents an efficient traffic management strategy designed to enhance the flow of AGVs within a Flexible Manufacturing System (FMS). This approach is evaluated through a comparative analysis with current state-of-the-art methods, demonstrating its effectiveness in optimizing traffic flow and minimizing delays.Secondly, the thesis delves into the practical application of the proposed traffic management strategy in real-world industrial settings. It assesses the spatial discretization of the AGV workspace and control periods in the implementation of the traffic management strategy in industrial environments, providing valuable insights crucial for bridging the gap between theoretical models and their practical deployment, hence ensuring effective integration into existing industrial processes.Lastly, the research investigates the impact of different task assignment criteria on the overall efficiency of the AGV system. By examining various strategies for allocating tasks to AGVs, the thesis identifies key factors that influence the performance of both traffic management and the entire transportation system. This analysis aims to refine task assignment methods to further improve the operational efficiency of AGV fleets, ultimately contributing to more streamlined and productive industrial workflows.
  

DOCTORAL DEGREE IN BIOMEDICAL ENGINEERING

• JAMES, CHRISTOPHER WILLIAM VINCENT: Biomaterials for Cardiac Regeneration
  
  Author: JAMES, CHRISTOPHER WILLIAM VINCENT
  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 Materials Science and Engineering (CEM)
  Mode: Normal
  Deposit date: 01/10/2025
  Reading date: 26/11/2025
  Reading time: 16:00
  Reading place: Sala d'Actes, Edifici Vèrtex, Campus Diagonal Nord, Vèrtex (VX), Plaça d'Eusebi Güell, 6, 08034 Barcelona
  Thesis director: ENGEL LOPEZ, ELISABET
  Thesis abstract: Cardiovascular diseases are the leading cause of death globally, with heart transplantation being the most effective treatment following injury due to the heart’s limited regenerative capacity. In situ tissue engineering has emerged as a promising approach to activate endogenous cardiac repair. This thesis focuses on the design, development, and characterization of injectable biomaterials for in situ cardiac regeneration.Hydrogel scaffolds that physically support damaged tissue, provide inherent bioactivity, or deliver bioactive agents are particularly promising. We first isolated and characterized porcine cardiac extracellular matrix (cECM) hydrogels, demonstrating their compatibility for supporting the growth of cardiac-associated cells. To enhance the viscoelastic properties of cECM without compromising biocompatibility, alginate—a hydrogel currently in clinical trials—was incorporated, resulting in improved mechanical properties.Lactate, traditionally seen as a metabolic by-product, has gained attention for its role in promoting angiogenesis, cardiomyocyte proliferation, and reducing fibrosis. Based on this evidence, we developed a lactate-release scaffold by embedding FDA-approved PLGA nanoparticles into the cECM-alginate matrix, optimizing nanoparticle size and degradation to achieve sustained lactate release.The regenerative potential of stem cell-derived secretomes, comprising bioactive molecules such as growth factors and extracellular vesicles, was also explored. Bone marrow-derived stromal cell (BMSC) secretomes were evaluated for their cardioprotective effects on human cardiac fibroblasts. A novel culture method showed superior outcomes, and the resulting secretome was incorporated into the scaffold either directly or via PLGA nanoparticles for sustained delivery.In conclusion, this work presents several novel injectable biomaterials that show potential for in situ cardiac regeneration through enhanced mechanical support, bioactivity, and sustained delivery of regenerative agents such as lactate and BMSC-derived secretomes. These findings warrant further investigation to optimize the therapeutic efficacy of these platforms.
  
• LÓPEZ GÓMEZ, PATRICIA VICTORIA: Multifunctional hydrogels for advanced regenerative therapies
  
  Author: LÓPEZ GÓMEZ, PATRICIA VICTORIA
  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 Materials Science and Engineering (CEM)
  Mode: Normal
  Deposit date: 19/09/2025
  Reading date: 11/12/2025
  Reading time: 10:30
  Reading place: Aula A1.13, Edifici A, Escola d'Enginyeria de Barcelona Est (EEBE), Av. d'Eduard Maristany, 16, 08019 Barcelona
  Thesis director: MAS MORUNO, CARLOS | MEHWISH, NABILA
  Thesis abstract: Implant-associated infections remain one of the most critical challenges in the biomedical field. Despite advances in aseptic surgical techniques and antibiotic therapies, the persistence of bacterial colonization on implant surfaces -often involving biofilm formation- continues to compromise clinical outcomes. Conventional treatment strategies, including the systemic administration of antibiotics, local drug delivery systems, and surgical debridement, often fail to effectively eradicate biofilms, particularly those formed by pathogens such as Staphylococcus aureus and Pseudomonas aeruginosa. Concurrently, the field of tissue engineering demands implantable materials that not only support tissue regeneration but also provide active defense against infection. However, most currently available materials fall short of achieving this dual function. In contaminated or high-risk environments, this shortcoming becomes especially critical. To address these limitations, there has been a growing focus on developing next-generation biomaterials that are no longer passive scaffolds but bioactive and dynamic systems capable of interacting with the biological milieu in real time. In this context, biofunctionalization has emerged as a powerful strategy to enhance both regenerative and antimicrobial properties of biomaterials. Among the different bioactive tools available, peptides have shown considerable promise due to their tunable chemistry, modular architecture, and high specificity. This thesis focuses on two peptide motifs with complementary bioactivities: RGD, which promotes cell adhesion, and hLf1-11, a broad-spectrum antimicrobial peptide derived from human lactoferrin. Together, these peptides represent a rational platform for engineering multifunctional materials that address the dual challenge of infection control and tissue integration. Specifically, the present work investigates the integration of RGD and hLf1-11 peptides into three hydrogel-based material systems, each representing a distinct level of biofunctionality and design complexity: • Chapter I examines the modification of alginate, a naturally derived but bioinert polymer, with RGD-hLf1-11 to evaluate stem cell behavior and antimicrobial activity. • Chapter II explores a fully synthetic PEG-based hydrogel system functionalised with these peptides and incorporating a protease-sensitive crosslinker to enable bioactivity and controlled degradation. • Chapter III focuses on self-assembling peptide hydrogels, where both structural assembly and biological function are encoded at the molecular level, enabling the formation of intrinsically bioactive materials without further modification. Collectively, these platforms offer a comparative and progressive approach toward the design of multifunctional hydrogels. This work not only demonstrates the feasibility of dual viii biofunctionalization but also highlights the critical roles of molecular architecture, crosslinking strategy, and material origin in shaping biological responses. Ultimately, this thesis contributes to the development of smart biomaterials that are both cell-instructive and antibacterial, aligning with the growing clinical need for adaptable, multifunctional solutions in regenerative medicine and infection-prone environments.
  

DOCTORAL DEGREE IN BUSINESS ADMINISTRATION AND MANAGEMENT

• SHEN, SHUYI: Chinese Expatriate Employee Perceptions of Talent Management
  
  Author: SHEN, SHUYI
  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 BUSINESS ADMINISTRATION AND MANAGEMENT
  Department: Department of Management (OE)
  Mode: Normal
  Deposit date: 15/10/2025
  Reading date: pending
  Reading time: pending
  Reading place: pending
  Thesis director: GALLARDO GALLARDO, EVA | FERNANDEZ ALARCON, VICENÇ
  Thesis abstract: In recent years, the global expansion of Chinese multinational corporations has led to an increasing reliance on expatriate assignments. Despite the growing strategic role of these assignees, little is known about how they perceive their status within talent management systems. This study investigates how Chinese expatriate employees are identified by organizations and how they interpret their inclusion—or exclusion—in organizational TM initiatives based on their perceptions of organizational justice.Adopting a longitudinal, qualitative, and inductive research design, this study draws on initial in-depth interviews with 36 early-career developmental expatriates, followed by a second round of interviews with 19 selected participants within a 12-month period. Grounded in theories of identity construction, identity work, and social comparison, the study investigates how perceptions of TM evolve over time and how expatriates make sense of their talent status across global postings.The findings reveal inconsistencies in talent identification practices, including varied selection criteria across acquisition channels and a lack of alignment between organizational and individual definitions of talent. While headquarters’ classification of expatriates as “talent” serves a symbolic sensegiving function, employees perceive distributive justice primarily through the developmental resources they receive, rather than the talent label itself. Conversely, those excluded from talent pools engage in identity reconstruction and adopt adaptive coping strategies.Additional insights highlight the positive impact of inclusive TM practices and expose tensions arising from dual-track systems in host-country subsidiaries. The study also identifies several mechanisms that undermine expatriates’ talent identity construction and increase turnover risk, including excessive role ambiguity, superficial sensegiving, rigid hierarchical cultures, and insufficient intercultural capacity-building.By shifting the analytical focus from expatriate task performance to long-term identity construction and career development, this research offers novel insights into the global talent management practices of CMNCs. It contributes to the intersection of global talent management and organizational justice by contextualizing TM within Chinese organizational environments. The study calls for more transparent, inclusive, and development-oriented TM systems that account for the evolving identity and motivational needs of expatriates in international assignments.
  

DOCTORAL DEGREE IN CHEMICAL PROCESS ENGINEERING

• RODRÍGUEZ ALEGRE, RUBÉN: Wastewater treatments in real case studies: separation & purification in the framework of the circular economy
  
  Author: RODRÍGUEZ ALEGRE, RUBÉN
  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 CHEMICAL PROCESS ENGINEERING
  Department: Department of Chemical Engineering (EQ)
  Mode: Article-based thesis
  Deposit date: 02/10/2025
  Reading date: 03/12/2025
  Reading time: 11:00
  Reading place: Sala Polivalent I - Edifici IEEBE - Campus Diagonal Besòs https://eebe.upc.edu/ca/lescola/com-arribar
  Thesis director: PEREZ MOYA, MONTSERRAT | GARCIA MONTAÑO, JULIA | YOU CHEN, XIALEI
  Thesis abstract: This thesis explores innovative strategies for resource recovery and water reuse from wastewater streams in three key sectors (agrifood, industrial, and urban) by integrating membrane technologies and chemical precipitation. While membrane and chemical treatments have been widely studied independently, this work is among the first to demonstrate their combined and sector-specific application in real or pilot-scale scenarios, framed within circular economy principles.In the agrifood sector, pig slurry liquid fraction was used as a representative effluent. This thesis is the first to evaluate both acidification and basification strategies for nutrient recovery from SLF using a membrane-based treatment train. Acidification, combined with microfiltration and forward osmosis, enabled >80% recovery of NPK nutrients. In contrast, basification allowed for selective nitrogen recovery using precipitation and membrane-assisted stripping. From these strategies, the basic treatment was validated under real on-farm conditions over two years, demonstrating its seasonal robustness and nitrogen recoveries between 46–56%. Water recovery for reuse in irrigation ranged from 39–75%, confirming the viability of decentralised systems in rural settings and marking a significant advance beyond laboratory-scale approaches.For the industrial sector, this thesis presents a novel circular treatment train for acid mine drainage, integrating reverse osmosis with bipolar membrane electrodialysis and chemical precipitation, a combination not previously applied to this matrix. This enabled the selective recovery of high-value metals (Al, Zn, Cu, Mn, Mg, Ca) with >60% efficiency, along with 97% water recovery and in-situ regeneration of NaOH. This approach shifts the paradigm from pollutant removal to resource valorisation, offering a replicable model for sustainable mining wastewater treatment with chemical reuse loops.In the urban context, microplastic pollution was addressed through the first systematic study on how microfiltration membrane configuration affects recovery efficiency. Using synthetic wastewater and varying spacer geometries and sizes, it was found that smaller diamond or corrugated geometries significantly improved performance, with recovery rates >99% and water reuse at 80%. This work provides practical design guidance for future applications in microplastic retention under real wastewater conditions.Additionally, circularity indicators (resource and water recovery) were applied to each case study, allowing for a quantitative assessment of circular performance. All recovered products and waters were analysed following the current Spanish legislation, confirming their potential reuse in irrigation, industrial, and cleaning applications. Overall, this thesis not only demonstrates the technical feasibility of integrated membrane–chemical systems, but also establishes their role as scalable, circular solutions for wastewater treatment across multiple sectors.
  

DOCTORAL DEGREE IN CIVIL ENGINEERING

• ALARCÓN FERNÁNDEZ, DANIEL: A model for the aero‐hydro‐servo‐elastic analysis of floating offshore wind turbines based on a co‐rotational formulation
  
  Author: ALARCÓN FERNÁNDEZ, DANIEL
  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: 12/09/2025
  Reading date: 14/11/2025
  Reading time: 12:00
  Reading place: UPC Campus Nord, ETSECCPB, C/ Jordi Girona 1-3, edifici C2, Sala Conferències 212, Barcelona
  Thesis director: MOLINS BORRELL, CLIMENT
  Thesis abstract: Nowadays, there is an increasing, despite reduced, number of models capable of performing fully coupled aerohydro‐servo‐elastic simulations in the time domain for the analysis of floating offshore wind turbines (FOWTs).Historically, in its beginnings, these models widely adopted rigid multibody systems (RMS) formulations todescribe the global dynamics response of the complete system. However, their incapability to determine theinternal stress‐strain state of hyperstatic components, in conjunction with the irruption of platform conceptswith higher structural complexity, promoted the development of a second generation of models adoptingflexible multibody systems (FMS) formulations. Whose main strategy, because they were fundamentally anevolution of the firsts, relied on describing the dynamic response of the flexible components by superimposinga first‐order deformational analysis over their spatial rigid‐body configuration. Nevertheless, because theindustry has quickly trended in the last decades toward bigger and more powerful wind turbines, somecomponents of the system have suffered from increasing slenderness and flexibility. As is the case of the rotorblades or the tower, which are starting to require the adoption of non‐linear analyses to assess their dynamicresponse and their internal stress state properly.In this context, there is an incipient but reduced number of models capable of performing fully coupled nonlineardynamic structural analyses of FOWTs. However, they are mostly strictly restricted to one‐dimensionalbeam type elements, forcing the adoption of approximated local load mapping procedures during the detailedengineering design phase. For that reason, a new advanced fully coupled model based on the Finite ElementsMethod (FEM) is proposed in the present thesis. Its main advantages lie in the ability to perform non‐lineardynamic analyses in time domain of complex structural models composed of multiple finite elements ofdifferent nature. This feature allows a more precise definition of the real structural behaviour and, therefore,leads to more detailed internal stress‐strain state analyses without the need of adopting additional techniques.The underlying balance equations of the model have been derived based on the Element Independent Corotational(EICR) method, whose foundations were laid in the work developed by C. C. Rankin and F. A. Broganin the 1980s and later readapted and improved by C.A. Felippa and B. Haugen in the 2000s. However, becauseit was initially mainly focused on non‐linear quasi‐static structural analysis, a detailed and consistent extensionto non‐linear dynamics based on continuum mechanics theory has been developed in the framework of thepresent thesis research.To evaluate the performance of the proposed structural model, it has been verified based on a set ofcomputational mechanics benchmarks available in the literature on non‐linear dynamics of flexible bodies.While the fully coupled aero‐hydro‐servo‐elastic model for the analysis of FOWTs has been validated based onthe experimental data provided in the framework of the Offshore Code Comparison, Collaboration, Continued,with Correlation and unCertainty (OC6) international project promoted by the International Energy Agency(IEA).
  
• MARTORELL PONS, LLUÍS: IGA application on crashworthiness CAE analysis including advanced plasticity and ductile fracture
  
  Author: MARTORELL PONS, LLUÍS
  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: 16/10/2025
  Reading date: 01/12/2025
  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 | BARBU, LUCIA GRATIELA
  Thesis abstract: In the contemporary automotive engineering landscape, increasing demands for efficiency, safety, and sustainability have intensified the pressure on development processes. The reduction of the overall duration of the development of a new vehicle is a key focus for the manufacturers. Despite significant advancements in both Computer Aided Design (CAD) and Computer Aided Engineering (CAE) technologies, these domains remain disconnected, resulting in information loss, workflow inefficiencies, and extended development cycles. While Isogeometric Analysis (IGA) has emerged as a promising approach to bridge this divide by maintaining geometric exactness throughout the analysis chain, its practical implementation in industrial contexts remains low, mainly relegated to investigation on its use but not application in actual production projects.This thesis analyzes the automotive CAD and CAE processes to identify current barriers to IGA implementation by addressing the full spectrum of implementation challenges, from technical limitations to workflow integration and cultural adoption barriers. The focus is on crashworthiness applications but Noise, Vibration, and Harshness (NVH) applications is also explored. The methodology adopted in this work follows a multi-faceted approach. First, a historical analysis of CAD and CAE evolution in automotive applications reveals the fundamental origins of their disconnection. Second, the industrial material testing and modeling for crash simulations is explored, a novel material characterization framework is developed, introducing the Non-Isochoric Plasticity Assessment (NPA) methodology to identify when materials deviate from the traditional assumptions of plasticity, and the use of pressure-dependent plasticity models. Third, numerical investigations identify and analyze the "Cross-Talk effect" in trimmed IGA, culminating in the development of a detection algorithm. Finally, case studies demonstrate the application of IGA in real automotive components, establishing workflow guidelines for industrial implementation.This research aims to reduce the gap between academic IGA research and industrial IGA application by providing insights for both communities. For academic researchers, it highlights the technical, operational, and cultural barriers that currently prevent widespread implementation. For industrial practitioners, it offers a roadmap for incremental IGA adoption that respects existing workflows while leveraging the advantages of the technology. By addressing both theoretical capabilities and practical limitations, this work establishes a foundation for more integrated automotive development processes that can respond to the increasing complexity of vehicle design and performance requirements.
  
• PERELLÓ RIBAS, RAFEL: Data assimilation for real-time dynamic prediction of wind-induced forces in vehicle platooning
  
  Author: PERELLÓ RIBAS, RAFEL
  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: 01/10/2025
  Reading date: 17/11/2025
  Reading time: 14:00
  Reading place: UPC Campus Nord, ETSECCPB, C/ Jordi Girona 1-3, edificio C1, Sala 002, Barcelona
  Thesis director: HUERTA CEREZUELA, ANTONIO | ZLOTNIK MARTINEZ, SERGIO
  Thesis abstract: We consider the vehicle platoon problem from an aerodynamic point of view. That is, the multiparametric problem of predicting the aerodynamic forces acting on a vehicle following another one under realistic road conditions. We develop a RANS methodology to simulate accurately the physics governing the problem and devise a multifidelity methodology to approximate the problem using as few computational resources as possible. In the first part of the thesis we develop and analyse the surrogate methodology in the framework of the Smolyak approximation method. We extend the Multi-Index Stochastic Collocation (MISC) method to handle the problem in a more efficient way. This includes the possibility of constructing a single surrogate for vector-valued functions as well as the use of a parametric domain with categorical variables representing a finite number of possible leading vehicle geometries.For this, we formulate the Smolyak approximation in a more abstract way and implement it in a modular C++ code using efficient and stable numerical algorithms. We also provide some novel convergence bounds of the method and validate them with numerical examples. We also address the problem of unstability that manifests in some problems where MISC has been applied in the form of spurious oscillations. We identify the reasons of the oscillations as irregularities present in the low fidelity data and prove a theorem in the general framework of multifidelity that shows that such irregularities must be avoided to ensure convergence of the surrogate.In the second part of the thesis we study different efficient CFD methodologies to simulate accurately the flow past a vehicle in realistic road conditions. This includes the presence of cross-wind and the disturbances of the flow due to other vehicles in typical platoon and overtake manoeuvres. Finally we apply the extended MISC method to a multiparametric platoon problem to construct a surrogate of the three components of the aerodynamic force acting on a vehicle under realistic platoon conditions.
  
• PRATS PUNTÍ, ARNAU: Estudi experimental de la resistència al flux de la canya americana (Arundo Donax) en condicions de vegetació emergent. Aplicació a rius mediterranis.
  
  Author: PRATS PUNTÍ, ARNAU
  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: 26/09/2025
  Reading date: 18/11/2025
  Reading time: 15:00
  Reading place: UPC Campus Nord, ETSECCPB, C/ Jordi Girona 1-3, edificio C1, Sala 002, Barcelona
  Thesis director: FERRER BOIX, CARLES | NUÑEZ GONZÁLEZ, FRANCISCO
  Thesis abstract: The massive presence of giant reed (Arundo Donax) in river channels leads to an increased flood risk. In riparian areas with a Mediterranean climate, this invasive plant of great height and spatial density represents a significant increase in flow resistance as it colonizes areas previously occupied by native vegetation or bare alluvial soil. Arundo Donax’s flow resistance was not quantified and knowing it is necessary to calculate the hydraulic capacity of river channels where it is present, and thus improve flood risk management.The main characteristics of the plant necessary for its study were obtained from a fieldwork carried out in a riparian stand located in the Llobregat River (near Barcelona). The stems have an average height of 6 m and the spatial density is very variable, with an average value of 23 m2 but with a maximum of 78 and a minimum of 4. The leaves grow only in the upper half of the plants, and for the lower part the average frontal area of opposition per unit volume (a) is 0.575 m-1 and the solid fraction is 0.011.Due to the large size of the plant, an experimental study was conducted in a laboratory flume using a physical scale model based on Froude similarity. The study focused exclusively on emergent vegetation conditions (that is when the plant height exceeds the water depth and occupies the entire water column) and therefore stem bending is irrelevant and is not represented. The geometric scale of the model is 8 and the stems are represented in the flume by rigid steel cylinders with a diameter of 3.4 mm.The main objective is to determine the drag coefficient (CD) of the extensive Arundo Donax's stands under emergent vegetation conditions. With this coefficient, the drag force and flow resistance can be calculated for any water depth below 3 m, which is the maximum threshold at which the assumed hypotheses are applicable.The spatial distribution of the stems obtained in the fieldwork was represented in the flume (heterogeneous model). To analyze the effect of a highly variable spatial distribution of obstacles on the flow, a second model was installed for comparison. This second model (homogeneous model) has the same characteristics and number of cylinders, but with the difference that they are placed in a staggered pattern. The analysis of the velocity measurements obtained with ADV was carried out by applying their temporal and spatial average (Double-Averaging Method). The momentum balance, necessary for the calculation of CD, was calculated with the Double-Averaged Navier-Stokes equations.For the heterogeneous model, the average result of CD is 1.06 and no clear relationship between CD and flow velocity is observed for the tested velocity range (0.26-0.81 m/s, prototype scale). The result of the homogeneous model is higher (1.34 on average), and means that the heterogeneous spatial distribution of the stems causes their drag force and flow resistance to be lower than if they were located following a homogeneous pattern. The vertical profiles of the longitudinal velocity do not have a logarithmic shape but are rather constant.The results are intended to be applied in streams with a massive presence of Arundo Donax’s stands, using a flow resistance equation valid under emergent vegetation conditions which uses the product of the variables CDa equal to 0.61 m-1, obtained in this research. For the stands, the larger the water depth the larger the flow resistance. Flow resistance values obtained are much higher than those of the alluvial bed or other riparian species, thus demonstrating Arundo Donax’s great impact on hydraulics and flood risk. Application of the results was conducted in two real case studies. For a small stream full of stands that crosses an urban area, the estimated hydraulic capacity is reduced by 60%.
  
• RENDON DÁVILA, VÍCTOR OSCAR: COMPORTAMIENTO HIDRÁULICO DE ALIVIADEROS DE PERFIL ESTRICTO EN ZONAS DE GRAN ALTITUD
  
  Author: RENDON DÁVILA, VÍCTOR OSCAR
  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: 01/10/2025
  Reading date: 10/11/2025
  Reading time: 12:00
  Reading place: UPC Campus Nord, ETSECCPB, C/ Jordi Girona 1-3, edificio C1, Sala 002, Barcelona
  Thesis director: SANCHEZ JUNY, MARTI
  Thesis abstract: This research explores the influence of altitude on the shape of strict profile spillways and their discharge coefficients, pressure field, and cavitation risk. An experimental campaign has been carried out next to the Condoroma dam, in Peru, at an altitude of 4075 m a.s.l. and the data obtained were compared with existing classical references. First, the influence of altitude on the spillway profile was analyzed. For this purpose, the discharge over a sharp crested rectangular spillway was analyzed, considering 5 discharge heights of 0.05, 0.10, 0.20, 0.20, 0.30, and 0.35 m and flow rates up to a maximum of 285 l/s. Classical fits by Creager (1917), Scimemi (1930), Creager et al. (1945), Hager (1987), and WES (1977) show some differences with respect to the profile resulting from the Condoroma experiments for all P⁄Hd ratios.The equation proposed for the Condoroma data allows the standard profile of a spillway to be defined at altitudes around 4000 m a.s.l. Near the ridge 0<x⁄H_d <0.5, for dimensionless profiles, there is a tendency for the Condoroma values to overlap with the classical profiles. For x⁄Hd >1, the Condoroma profile, it tends to separate from all the classical profiles, giving a slightly wider profile. In order to evaluate the discharge coefficients, up to five different spillway heights (P), characterized by the dimensionless value (P⁄Hd ) were analyzed. The results show that the discharge coefficients vary considerably, with values systematically lower than those obtained to date in previous studies at lower altitudes. With regard to the pressure field and the risk of cavitation in standard profile spillways designed according to the USBR criteria for the 5 different heights, the results are presented in standardized graphs and compared with previous studies, showing that the pressure ranges obtained are similar. In addition, the structural recommendations for the design of these spillways are compared, and it is found that in most cases they are more restrictive than in areas of lower elevation.With regard to the risk of cavitation, new plots are presented with for P⁄H_d showing that there is a critical value of H⁄Hd and that this is more limiting than the pressure load for all the P⁄Hd studied.
  

DOCTORAL DEGREE IN COMPUTATIONAL AND APPLIED PHYSICS

• CONESA ORTEGA, DAVID: Empirical and Structural Mathematical Models for Biological Systems: Case Studies in COVID-19 and Cardiac Dynamics
  
  Author: CONESA ORTEGA, DAVID
  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 COMPUTATIONAL AND APPLIED PHYSICS
  Department: Department of Physics (FIS)
  Mode: Normal
  Deposit date: 21/10/2025
  Reading date: pending
  Reading time: pending
  Reading place: pending
  Thesis director: ALVAREZ LACALLE, ENRIQUE
  Thesis abstract: In the diverse and complex world we live in, we ask ourselves how everything that surround us works. We aim to understand what, how, why, when, and, in this context, scientists started to use mathematical language to model and explain the events of this world. Biology encompasses many different topics, with multiple scales, and the types of models used for their study vary from one to the other.In this thesis we elaborate empirical and predictive mathematical models, mechanistic models as well, to study and analyze two branches of biology: epidemiology, in the context of a pandemic like COVID-19, and cardiac dynamics.To start, we develop predictive, Gompertz-like models to predict two weeks in advance the increase of the incidence of COVID-19, based on country-level reported data from WHO. In this chapter, we analyze the reliability and accuracy of such models with different processing to correct certain patterns due to possible inconsistencies in the daily reports during the most tense times of the pandemic.Continuing with epidemiology, in this thesis we also perform a study of correlation between incidence of COVID-19 in the Spanish society, province by province, and mobility data from different sources: the Spanish Ministry of Transport and Mobility and Facebook Data For Good. Using tools like the Principal Component Analysis, we determine what data correlate the most with incidence, either workdays or weekends mobility, or temperature or humidity. Results indicate that mobility is either directly causal or it is highly, directly correlated with other measures that affect propagation, whereas meteorological patterns seem less relevant by themselves.Turning to cardiac dynamics, this thesis has a focus on the development of computational models aiming to study calcium dynamics in cardiomyocytes for its future analysis in relationship to cardiac diseases. On the one hand, we develop a model of rabbit atria mixing two models: one developed previously by the same author focused on the spatial dynamics of calcium, and one developed by Holmes focused on ionic currents in the membrane. During the process, using a population-of-models approach, we determine some unknown parameters for the RyR2, NCX and SERCA currents that give rise to models behaving like experimental data usually observed. Moreover, during the process, we get diverse groups of models with different behaviors between them, useful to study cells in conditions more susceptible to disease.Last but not least, we develop another model at submicron scale to analyze how calcium waves originate and what type. In particular, we study scenarios where calsequestrin is either colocalized or it is not with RyR2, or how inactivation of RyR2 by calmodulin affects wave propagation. The study unveils that colocalization is key and vital for wave propagation. Inactivation of RyR2 by calmodulin allows the wave to travel more rapidly and hinders the appearance of another equilibrium state with an excessive calcium in the cytosol and low calcium load in the sarcoplasmic reticulum.To conclude, this thesis contributes to the study of two completely different fields in biology from the point of view of different mathematical models, always with the aim to understand and prevent causes leading to disease.
  
• RIU I VICENTE, JORDI: Scaling Quantum Optimization Algorithms: Advancing Techniques for Handling Industrial-Level Workloads with Artificial Intelligence
  
  Author: RIU I VICENTE, 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 COMPUTATIONAL AND APPLIED PHYSICS
  Department: Department of Physics (FIS)
  Mode: Normal
  Deposit date: 01/10/2025
  Reading date: pending
  Reading time: pending
  Reading place: pending
  Thesis director: GARCIA SAEZ, ARTURO | MONRAS BLASI, ALEXANDRE
  Thesis abstract: This thesis integrates traditional optimization methods and artificial intelligence techniques with near‑term quantum algorithms to accelerate the adoption of hybrid protocols for large‑scale, time‑sensitive decision problems. These techniques can manage millions of variables, but they are very expensive computationally. Conversely, quantum approaches such as quantum annealing and QAOA promise more efficient exploration of complex solution landscapes, yet they are hampered by severe hardware limitations and steep training challenges like barren plateaus. By combining these paradigms, the work offloads subproblems suited to classical resources and reserves quantum circuits for the most demanding kernels.We first present the formulation of a relevant use case of a medical‑drone logistics network modeled across Catalonia’s mountainous terrain. An initial discrete‑time MILP with millions of binary variables captures the relevant properties but exceeds the reach of current quantum hardware. A continuous‑time reformulation later reduces variable count. Resource estimates confirm that hardware scaling alone cannot close the gap, underscoring the need for new algorithmic strategies.To address NISQ‑era circuit depth, the thesis introduces RL‑ZX, a reinforcement‑learning–driven quantum compiler built on the ZX‑Calculus framework. Quantum circuits translate into feature‑annotated ZX diagrams, which a Graph Attention Network encodes. An agent trained with proximal policy optimization then applies ZX rewrite rules to minimize two‑qubit gates under device‑specific cost metrics. When evaluated on large, unseen circuits, RL‑ZX outperforms leading heuristics almost universally, yielding shallower, higher‑fidelity circuits.Next, we automate the process of including hard constraints in QUBO formulations. We design a Graph Neural Network and train it to predict penalty magnitudes in a single inference pass, treating variables and constraints as nodes and edges annotated with energy‑difference features. Integrated end‑to‑end with a differentiable QAOA simulator, this approach uses ground‑state sampling probability as its training signal. Experiments on random instances of the knapsack problem, TSP, and assignment tasks demonstrate large improvements in solution probability compared to analytic bounds.The third topic under study is the choice of starting state for VQAs. By measuring coherence via relative entropy, we show that higher‑coherence initial states produce better approximation ratios in fixed‑depth QAOA on small Max‑Cut instances. A tensor‑network imaginary‑time evolution protocol generates Matrix‑Product States approximating pure Gibbs states, that are later mapped to shallow quantum circuits. This initialization strategy suggests a clear path toward improved performance as quantum hardware matures.Finally, two “constraint-satisfying” ansätze are developed to prepare valid solutions directly, eliminating penalty terms. The first builds assignments incrementally using multi‑controlled rotations, while the second creates superpositions of partial assignments before deterministically completing them with ancilla qubits.By uniting these advances—hybrid classical/quantum decomposition, RL‑driven compilation, GNN‑based penalty tuning, coherence‑aware initialization, and feasible‑only state preparation—the thesis lays a scalable, modular foundation for achieving quantum advantage in real‑world optimization.
  

DOCTORAL DEGREE IN COMPUTER ARCHITECTURE

• RODRIGUEZ FERRANDEZ, IVAN: Mixed software/hardware-based fault-tolerance techniques for complex COTS system-on-chip in radiation environments
  
  Author: RODRIGUEZ FERRANDEZ, IVAN
  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: 01/10/2025
  Reading date: 01/12/2025
  Reading time: 09:00
  Reading place: C6-E101
  Thesis director: KOSMIDIS, LEONIDAS | TALI, MARIS
  Thesis abstract: This thesis titled “Mixed Software/Hardware-based Fault-tolerance Techniques for Complex COTS System-on-Chip in Radiation Environments" explores the challenges and solutions for integrating high-performance Commercial Off-The-Shelf (COTS) System-on-Chip (SoC) technologies, specifically GPUs, into space applications. These automotive-grade systems offer significant computational capabilities but face unique challenges in radiation-intense environments typical of space. The research investigates these challenges and proposes solutions to enhance the reliability of such systems. A key component of the thesis involves the comprehensive evaluation of modern embedded GPUs under space-like conditions, including exposure to proton and heavy-ion radiation. This analysis identifies vulnerabilities such as Single Event Effects (SEE) , which can cause faults like Single Event Upset (SEU), Single Event Functional Interrupt (SEFI), and Single Event Latch-up (SEL). To support these evaluations, the author develops the OBPMark suite, an open-source benchmark tailored for assessing the performance and efficiency of GPUs in space-specific computational tasks. To address the faults identified, the thesis proposes innovative software-based fault mitigation strategies. These include the design of fault-tolerant GPU kernels and middleware solutions that improve error detection and recovery. The effectiveness of these methods is demonstrated through both simulation and radiation testing. Additionally, the research presents hardware-level innovations, such as the development of application-specific integrated circuits (ASICs) and specialized printed circuit boards (PCBs), to enhance system resilience without compromising performance. This work significantly contributes to the field of space computing by creating a robust framework for evaluating and mitigating radiation effects in complex COTS SoCs. It bridges the gap between the cost-effectiveness and performance of commercial technologies and the reliability demands of space-grade applications. The findings of this thesis pave the way for the adoption of high-performance embedded GPUs in future space missions.
  
• SERRACANTA PUJOL, BERTA: Accelerating the Cloud: An Application-Agnostic Approach to Network and Compute Optimization
  
  Author: SERRACANTA PUJOL, BERTA
  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: 19/09/2025
  Reading date: 21/11/2025
  Reading time: 10:00
  Reading place: Sala 103 - Aula Teleensenyament - Edif. B3 - Planta 1
  Thesis director: CABELLOS APARICIO, ALBERTO | RODRÍGUEZ NATAL, ALBERTO
  Thesis abstract: This thesis explores how to enhance the performance of cloud applications by addressing inefficiencies across both the network and compute layers of modern distributed systems. As cloud-native applications grow more complex and are deployed across heterogeneous, geographically distributed infrastructures, traditional abstractions, though foundational for scalability and modularity, have begun to constrain opportunities for global coordination and responsiveness. To overcome these limitations, this work introduces two complementary approaches: one that improves network resource utilization without requiring developer involvement, and another that enhances compute-side elasticity through a smarter, more proactive autoscaling mechanism. Both approaches are guided by a common design philosophy: introducing context-aware intelligence in a minimally disruptive way, maintaining full compatibility with existing architectures, infrastructure, and developer workflows.The first part of the thesis focuses on Network-Application Integration (NAI), specifically targeting performance improvements in inter-datacenter communication. To this end, it proposes an application-agnostic solution based on extended Berkeley Packet Filter (eBPF) and eXpress Data Path (XDP) technologies. By dynamically identifying and separating short and long Transmission Control Protocol (TCP) flows at the network ingress, the system enables differentiated routing through distinct network tunnels, thereby mitigating queuing delays and reducing flow completion times. A key advantage of this approach is that it operates transparently, requiring no modifications to applications or developer-provided annotations, making it highly deployable within existing environments. Testbed experiments demonstrate that this technique significantly reduces latency and improves resource utilization in hybrid, multi-datacenter scenarios.The second part of the thesis turns to the compute domain, focusing on autoscaling mechanisms in Kubernetes-managed microservice environments. Recognizing the limitations of existing reactive scaling strategies, the work develops a control-theoretic model of the Kubernetes Horizontal Pod Autoscaler (HPA), formally analyzing its stability and responsiveness. Based on these insights, a new context-aware HPA is introduced, which incorporates upstream CPU metrics from the application’s service graph to anticipate downstream load changes. This proactive strategy enables more efficient and stable scaling decisions, improving responsiveness and reducing latency during traffic spikes. Notably, it achieves these gains without relying on complex performance models or machine learning, preserving simplicity and compatibility with standard Kubernetes tooling.Overall, the two approaches presented in this thesis offer practical methods for improving the performance and efficiency of distributed cloud applications, with a focus on compatibility with existing systems and workflows. Rather than proposing disruptive architectural changes, both solutions extend current abstractions to introduce additional context-awareness where it can be most effective. The results suggest that incremental, deployable enhancements to orchestration and networking layers can help address emerging challenges in scalability and responsiveness, making them suitable candidates for integration into real-world cloud-native environments.
  

DOCTORAL DEGREE IN CONSTRUCTION ENGINEERING

• BENAVIDES ALCIVAR, JULIO DARIO: Thermo-mechanical performance of steel slag asphalt mixtures and their potential for urban heat mitigation
  
  Author: BENAVIDES ALCIVAR, JULIO DARIO
  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 CONSTRUCTION ENGINEERING
  Department: Department of Civil and Environmental Engineering (DECA)
  Mode: Article-based thesis
  Deposit date: 19/09/2025
  Reading date: 14/11/2025
  Reading time: 11:00
  Reading place: Sala Zienkiewich (CIMNE) Edifici C1, UPC - Campus Nord, Gran Capitán S/N 08034 Barcelona
  Thesis director: APONTE HERNÁNDEZ, DIEGO FERNANDO | LÓPEZ MONTERO, TERESA
  Thesis abstract: This doctoral thesis focuses on the study of steel slag from electric arc furnaces as a technically and environmentally viable substitute for natural aggregates in asphalt mixtures. While the primary objective was to evaluate its thermo-mechanical behaviour and potential for urban heat island (UHI) mitigation, other fundamental aspects were also addressed to validate its real-world application in sustainable urban pavements, such as moisture resistance, fatigue performance and cracking behaviour.Through a series of experimental investigations, structured as independent yet complementary chapters, the influence of steel slag on the physico-chemical, mechanical, and thermal properties of asphalt mixtures was analysed, along with its integration into embedded solar collector systems (ASC).Initially, the physical, morphological, and chemical properties of steel slag were characterised to understand its effect on aggregate–bitumen adhesion and moisture resistance (ITSR). Through bitumen affinity tests, digital image analysis, and ITSR testing, it was demonstrated that steel slag improves resistance to moisture damage—even under total replacement—due to its high surface roughness and its composition rich in metallic oxides.In a second stage, partial replacement of natural aggregates by steel slag in the fine fractions was evaluated. Similar benefits were observed in terms of moisture resistance, with density remaining within conventional ranges. This result suggests that the use of steel slag in fine fractions may overcome previous limitations related to increased total mix weight.Subsequently, the mechanical behaviour was addressed through indirect tensile strength, stiffness, and fatigue tests. Mixtures with different levels of slag replacement and bitumen film thicknesses (TF) were designed and evaluated using four-point bending and strain sweep (EBADE) tests. The results showed that increasing the slag content raises mixture stiffness but reduces fatigue resistance, an effect attributed to the material's hardness and its tendency to decrease the effective bitumen film thickness. However, a well-optimised mixture—such as HMA_SL* with a corrected TF—achieved performance similar to the control mixture, even under critical temperature conditions.Furthermore, crack resistance was assessed using the Fénix test, which showed that although slag-containing mixtures require more energy to initiate cracking, their post-failure behaviour tends to be more brittle. Nevertheless, the mixture optimised in terms of bitumen film thickness showed significant improvements in toughness (IT) and fracture energy (GF), highlighting that adjusting the binder content is essential to mitigate this limitation.In parallel, the thermal properties of the mixtures were investigated through both experimental tests and numerical simulations (FEM). It was observed that steel slag reduces the thermal conductivity of the mixture, slowing down its cooling rate and thus extending the compaction window. While this also lengthens the required cooling time prior to traffic opening, the simulations accurately predicted thermal evolution, facilitating better design and process control.Finally, the application of steel slag in asphalt pavements with embedded solar collectors (ASC) was explored, aimed at UHI mitigation and thermal energy recovery. Dense mixtures containing steel slag (AC16D + AC22D) exhibited favourable thermal balance, reducing surface temperature by up to 16.3 °C and improving heat collection efficiency compared to gap-graded mixtures with the same material (BBTM11-B + AC22D). This performance confirms its potential as a functional material for sustainable urban applications.Altogether, the findings of this thesis validate steel slag as a technically sound, functional, and environmentally competitive material for the development of both structural and functional asphalt mixtures, contributing to more sustainable urban paving practices.
  
• RAMIREZ PEREZ, ALEXIS JOHARIV: Comportamiento a flexión y cortante de un tablero continuo de vigas pretensadas con tendones de polímeros reforzados con fibras (FRP)
  
  Author: RAMIREZ PEREZ, ALEXIS JOHARIV
  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 CONSTRUCTION ENGINEERING
  Department: Department of Civil and Environmental Engineering (DECA)
  Mode: Normal
  Deposit date: 22/10/2025
  Reading date: pending
  Reading time: pending
  Reading place: pending
  Thesis director: OLLER IBARS, EVA MARIA | MARI BERNAT, ANTONIO RICARDO
  Thesis abstract: The durability of reinforced concrete structures is mainly compromised by steel corrosion, which generates high maintenance costs and reduces structural safety. Fiber-reinforced polymers (FRP) represent an alternative of great interest, as they provide high specific strength and are not susceptible to corrosion. However, their application as active reinforcement in continuous prestressed members is still very limited, due to the scarce experimental research on their structural performance and the absence of specific design guidelines.The main objective of this dissertation is to analyze the flexural and shear behavior of a two-span continuous bridge at 1/3 scale, built with precast prestressed girders and a cast in situ reinforced concrete slab, using carbon carbon fiber composite cables “CFCC” tendons as active reinforcement. The research was organized into three phases: (1) characterization of carbon fiber (CFRP) bars, glass fiber (GFRP) bars, and CFCC tendons, with the latter selected for prestressing due to their suitability; (2) a flexural test on span 1, with a concentrated load applied at midspan, to study the global flexural behavior at the serviceability and ultimate limit states; and (3) a shear test on span 2, with a concentrated load applied 1.6 m from the end support, to evaluate shear strength, effectiveness of GFRP stirrups, and the influence of CFCC prestressing. The results were compared with numerical simulations using the CONS program and with the CCCM analytical model adapted to FRP tendons. The experimental tests showed that CFCC tendons reached 62–76% of their ultimate strength without anchorage slip in the flexural test, confirming their reliability as active reinforcement. Failure was governed by shear-off at the girder–slab interface. In shear, failure occurred after a characteristic diagonal cracking pattern and progressive redistribution of stresses between spans, while shear-off failure was avoided through a reinforcement added after the flexural test.The overall contribution of this dissertation lies in providing the first comprehensive experimental, analytical, and numerical evidence on a continuous bridge prestressed with CFCC tendons. The findings strengthen confidence in the use of FRP in concrete structures, and open new research avenues aimed at optimizing transverse reinforcement and moving towards the codification of this technology.
  
• VALVERDE BURNEO, DAVID ENRIQUE: Desarrollo de nuevos materiales cementicios multifuncionales
  
  Author: VALVERDE BURNEO, DAVID ENRIQUE
  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 CONSTRUCTION ENGINEERING
  Department: Department of Civil and Environmental Engineering (DECA)
  Mode: Article-based thesis
  Deposit date: 10/10/2025
  Reading date: 19/01/2026
  Reading time: 11:00
  Reading place: C1-002
  Thesis director: SEGURA PEREZ, IGNACIO | GARCIA TRONCOSO, NATIVIDAD LEONOR
  Thesis abstract: This doctoral thesis focuses on the development of multifunctional cementitious materials, combining structural strength with self-sensing capabilities through piezoresistivity, as well as integrating deformation energy dissipation through auxetic structures. The research explores the integration of conductive and structural fibers in cementitious matrices, coupled with the use of advanced manufacturing techniques such as 3D printing and the use of flexible silicones to obtain molds with complex architectures. The objective is to obtain cementitious materials that in addition to possessing structural capacity, have added function capabilities. It is expected that these materials can be used in buildings with self-monitoring, damage prevention, stress sensing pavements, structural elements with higher impact resistance and energy dissipation capabilities. The research begins with an exhaustive bibliographic review, from which the most promising materials have been selected to achieve the proposed objectives. The experimental campaign and data treatment/analysis have been defined. The work continues with the realization of the planned experiments, the analysis of the results, the optimization of the composition and properties of the new cementitious materials, the development of prototypes testing the potential applications.From the achievements obtained in this doctoral thesis we have the following: the research and publication of a cementitious composite reinforced with recycled carbon fibers to obtain a piezoresistive conductive concrete, which presents a variation of the electrical conductivity with respect to the unitary deformation quite evident when the fiber addition contents are around 1% in volume. This makes it an ideal sustainable cementitious material for strain and/or stress detection. This publication can be found in the journal Construction and Building Materials.Another research focuses on the mechanical characterization of cellular auxetic cementitious cementitious composites (which achieve their auxeticity through the presence of ellipsoidal holes in their structure) reinforced with recycled steel fibers. This research successfully characterizes the influence of fiber content on the mechanical response to compression and deformation energy dissipation, while demonstrating the feasibility of using recycled resources. Within this same publication, a family of functions was presented that successfully fit the mechanical response curves (stress-strain, energy dissipated by deformation) that were obtained experimentally. This publication can be found in the Journal of Building Engineering.A third article achieved in this thesis deals with the development of a new type of piezoresistive concrete with auxetic capacity. This material, obtained by combining cellular auxetic cementitious cementitious composites and recycled carbon fibers, is capable of detecting deformations from very low to high levels. Its potential applications in structural monitoring are promising, and the results of this research have been published in Case Studies in Construction Materials.
  

DOCTORAL DEGREE IN EARTHQUAKE ENGINEERING AND STRUCTURAL DYNAMICS

• TIRADO GUTIERREZ, RODOLFO JAVIER: EVALUATION OF STRUCTURAL RELIABILITY. REVIEW OF DESIGN METHODOLOGIES AND SEISMIC PERFORMANCE EVALUATION OF REINFORCED CONCRETE STRUCTURES
  
  Author: TIRADO GUTIERREZ, RODOLFO 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 EARTHQUAKE ENGINEERING AND STRUCTURAL DYNAMICS
  Department: Department of Civil and Environmental Engineering (DECA)
  Mode: Normal
  Deposit date: 25/09/2025
  Reading date: pending
  Reading time: pending
  Reading place: pending
  Thesis director: VARGAS ALZATE, YEUDY FELIPE | GONZALEZ DRIGO, JOSE RAMON
  Thesis abstract: Seismic risk and structural reliability are fundamental concepts in the design and evaluation of safe and resilient buildings. It should be noted that most of the casualties, injuries and economic losses during an earthquake are associated with damage to civil structures. In this context, there is an urgent need to improve the design and evaluation methodologies of this type of structures. From a numerical perspective, several strategies can address this need, ranging from improvements in modeling and advanced analysis methods, to the probabilistic analysis of the variables involved (such as seismic hazard), including the review of configuration and material properties of the systems under study. Therefore, this research proposes a methodology to evaluate structural reliability using a probabilistic approach, validated through nonlinear dynamic analysis and a statistical cloud study. This methodology constitutes a robust and powerful tool, applicable not only at the building level but also at urban and regional scales. For this purpose, it is proposed to study a set of reinforced concrete buildings with variable configurations in both plan and elevation. These models represent real buildings, that were recently designed and constructed, located in areas of high seismicity in Colombia, for which the most modern seismic-resistant design standards have been followed. This thesis is divided into three main sections: 1) Calculation of improved intensity measures, in terms of efficiency and steadfastness, to derive more accurate fragility curves; 2) Development of a probabilistic analysis methodology to estimate, with high statistical accuracy and reduced time, the dynamic response of tall buildings by using transfer functions; and 3) Evaluation of structural reliability, based on the probability of exceeding different damage indices and thresholds at different levels of seismic intensity. The first part focuses on identifying and developing optimal and improved intensity measures, based on the efficiency and steadfastness they show as correlated with the structural response of complex systems. An optimal seismic intensity measure enables the development of more accurate fragility curves, which are essential for assessing the probability of damage in a structure under different seismic intensity levels. The second part focuses on the development of a structural analysis method based on the transfer function (TF) concept. This mathematical model establishes the relationship between the response of a system and the input excitation. The proposed approach allows probabilistic estimation, while maintaining statistical accuracy, of the nonlinear dynamic response. It aims to overcome the limitations of the high computational cost associated with nonlinear dynamic analysis. Finally, the third part focuses on calculating the structural reliability of two real buildings located in a high seismicity zone, evaluating the probability of exceeding different damage thresholds. The results obtained show that intensity measures based on velocity present a higher correlation with the structural response, regardless of whether they are analyzed as a whole. This will make it possible to evaluate risk scenarios in large areas by means of fragility curves that adequately represent different structural typologies, facilitating a better characterization of urban environments. Likewise, it is observed that the developed method, by using a reduced number of seismic records, allows obtaining reliable results in terms of the principal statistical moments of the structural response of complex systems, and importantly, in a considerably shorter time. In conclusion, this research presents a series of advanced numerical tools that allow the calculation of damage scenarios, while optimally accounting for seismic hazard, and using methodologies that significantly reduce the time required to estimate the structural reliability of a set of buildings.
  

DOCTORAL DEGREE IN ELECTRONIC ENGINEERING

• CAMÓS VIDAL, ROBERT: Design and characterization of an unobtrusive ECG monitoring system for wheelchairs
  
  Author: CAMÓS VIDAL, ROBERT
  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: 16/09/2025
  Reading date: 14/11/2025
  Reading time: 11:00
  Reading place: Aula de Teleensenyament de l'edifici B3 de l'ETSETB, campus nord
  Thesis director: ROSELL FERRER, FRANCISCO JAVIER | SUDRIA ANDREU, ANTONI
  Thesis abstract: This work was carried out within the framework of the “Doctorats Industrials” program, in collaboration with Regner Engineering S.L., a company specialized in the manufacturing of wheelchair solutions, and the Universitat Politècnica de Catalunya.As cardiovascular diseases (CVDs) remain the leading cause of death globally, and people with disabilities are at increased risk, the need for continuous, non-obtrusive heart monitoring becomes urgent. Three high-growth markets relevant to Electrocardiography (ECG) monitoring in wheelchairs were analyzed. First, the global wheelchair market is growing steadily, with powered models showing strong demand. Next, wearable and medical wearable markets are expanding rapidly, driven by advances in sensor integration and healthcare needs. Finally, the mHealth and IoHT sectors are experiencing major growth due to digital health trends and remote monitoring. Together, these markets highlight strong commercial potential for the proposed system.This PhD thesis presents the design and validation of a novel unobtrusive ECG monitoring system fully embedded into a wheelchair, tailored to the daily needs of individuals with mobility impairments.The developed solution integrates single-lead ECG sensors into the wheelchair’s armrests, using active electrodes powered by a bootstrapped supply to ensure ultra-high input impedance and high Common Mode Rejection Ratio (CMRR) in front of electrode impedance mismatch. This design allows the system to operate under both direct conductive contact (similar to dry electrodes) and indirect capacitive coupling (through clothing), without requiring hardware changes.Furthermore, the ECG sensor includes a protection circuit against electrostatic discharges (ESD), compliant with IEC 61000-4-2, which has been accurate designed and simulated in order not to degrade the high input impedance. The system also features Bluetooth connectivity and a modular backend, aiming for future scalability and industrial application.Sensor characterization was performed using an original experimental setup with an AC coupling inside a Faraday cage, allowing the measurement of very high input impedance values at low frequencies, i.e.191 fF at 50 Hz and common-mode rejection ratios (CMRR) up to 76.1 dB. Real-ECG recording tests with a volunteer wearing a cotton shirt confirmed accurate signal acquisition, with 117 µV RMS amplitude for the ECG and 31 dB of Signal to Noise Ratio (SNR).The research successfully achieved its goals by designing and validating a reliable unobtrusive ECG system for wheelchairs, meeting both clinical and industry standards. It lays a strong foundation for future developments in health monitoring. The proposed solution lays the foundation for future integration into chairs, beds, vehicle seats or even wearable technologies. It marks an important advance toward reliable, non-intrusive ECG monitoring for people with limited mobility, with both clinical and commercial potential.
  
• DE LA VEGA HERNÁNDEZ, JOAQUÍN: Advanced modelling and forecasting methods for electric vehicle batteries based on data analysis with realistic operating conditions.
  
  Author: DE LA VEGA HERNÁNDEZ, JOAQUÍN
  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: 22/10/2025
  Reading date: pending
  Reading time: pending
  Reading place: pending
  Thesis director: ORTEGA REDONDO, JUAN ANTONIO | RIBA RUIZ, JORDI ROGER
  Thesis abstract: This PhD thesis addresses the challenges of modeling, monitoring, and forecasting the behavior of lithium-ion batteries at both the single-cell and pack levels, with a particular focus on improving robustness under realistic operating conditions. The work is motivated by the growing deployment of battery systems in electric mobility and stationary storage, where performance, lifetime, and safety critically depend on reliable state estimation and degradation prediction.At the single-cell level, the thesis introduces two complementary modeling approaches: a parametric voltage–capacity model for constant-current discharge, and a deep learning framework for partial-charge data. These methods enable early detection of degradation trends and real-time estimation of capacity fade, providing interpretable health indicators (HI) and accurate remaining useful life (RUL) forecasts.At the pack level, the thesis explores the additional complexities that arise from cell-to-cell variability, imbalance, and data acquisition issues. A hybrid data imputation methodology based on the Unscented Kalman Filter (UKF) is proposed to reconstruct missing voltage signals at both the cell and branch levels, ensuring continuity of BMS functions such as balancing, SoC/SoH estimation, and fault detection. The method is benchmarked against neural networks, highlighting the trade-off between data-driven accuracy and model-based adaptability. Building on this, this work expands upon the imputation framework by studying how reconstructed signals impact the accuracy of forecasting models. Four reconstruction strategies of increasing complexity (ZOH, ARIMA, UKF, and GRU) were compared, and their outputs were fed into recurrent neural networks (LSTM and GRU) developed for this purpose. These networks were used to predict the remaining time to depletion (RTD) of individual cells under driving conditions. The results demonstrate that the quality of signal reconstruction directly impacts forecasting performance.The contributions are supported by multiple datasets, including public repositories (NASA, Sandia National Laboratories) and a custom experimental testbench capable of executing standardized drive cycles and controlled CC–CV protocols. Together, these datasets provide a rigorous foundation for validation across chemistries and cycling conditionsOverall, the thesis demonstrates how the integration of signal processing and filtering, and machine learning can enhance the reliability of battery models, both for immediate diagnostic tasks and long-term prognostics. The findings contribute toward more robust and practical battery management systems, bridging the gap between academic models and real-world applications in electric mobility and energy storage.
  
• KUMAR, DILEEP: Deep Learning for Improving Resilience of the Sensors in Mars Exploration Missions
  
  Author: KUMAR, DILEEP
  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: 22/10/2025
  Reading date: pending
  Reading time: pending
  Reading place: pending
  Thesis director: DOMINGUEZ PUMAR, MANUEL MARIA | PONS NIN, JOAN
  Thesis abstract: In space exploration missions, scientists have been putting efforts to achieve targeted scientific goals, such as environmental study of Mars through various exploration missions. In recent years, multiple missions have been sent to study the atmosphere of Mars with advance sensing systems. In the process of developing various sensing systems, worst- case scenarios are also considered and their possible solutions become crucial to be implemented to deal with adversities. During the operation of a mission, critical system components such as sensors could face complete or partial damage, and because of that, the mission might fail to send data to the monitoring station. This is a known fact in the case of wind sensors deployed with Mars 2020 Perseverance and Curiosity Rover missions those suffered partial damage. Moreover, at some point, TWINS wind sensor with InSight lander mission also faced power issues. Such scenarios create hurdles in the scientific study of a planet. Various space sensors related problems can be caused by various adversities, such as dust devils at Mars, adaptation of operating points in sensors themselves, and obstacles around the sensing systems. Furthermore, it is not possible to repair or replace a sensor on Mars. Thus, it is crucial to address space sensor problems with remedial techniques to achieve the target objectives.Considering problems occurred in Mars wind sensors, this thesis is focused on investigating data-driven approaches to improve resilience of Mars wind sensors deployed in the last two Mars missions of NASA, namely TWINS (InSight Mission, 2018) and MEDA (Perseverance Mission, 2020) instruments. Various Machine Learning (ML) and Deep Learning (DL) models have been investigated to enhance the resilience of the aforementioned wind sensors in the case of partial failure. These data-driven algorithms are investigated to develop a soft or virtual sensor for Martian wind sensors. Furthermore, Transfer Learning (TL) based approach has been investigated to deal with data scarcity scenarios. These methods have yielded promising results in recovering the data in the event of partial failure. In TWINS investigation, RMSE for velocity is reduced by a factor between 2.43 and 4.78; and for wind angle by a factor between 1.74 and 4.71, compared to the case where only two wind sensing transducers are functioning. For MEDA, the investigated algorithms allowed to recover variables of the wind sensing boards with errors similar to TWINS instrument and in some cases have achieved slightly better results. With the TL approach, the multivariate predictions improve with the RMSE percentage between 10.21% to 22%. In summary, various data-driven methods investigated have illustrated the efficacy and potential in recovering data and dealing with adverse scenarios related to Mars wind sensors.
  

DOCTORAL DEGREE IN ENGINEERING, SCIENCES AND TECHNOLOGY EDUCATION

• MIRÓ MEDIANO, ÀLEX: Defining, Modelling, and Sequencing Complexity in Secondary Mathematics
  
  Author: MIRÓ MEDIANO, ÀLEX
  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 ENGINEERING, SCIENCES AND TECHNOLOGY EDUCATION
  Department: Institute of Education Sciences (ICE)
  Mode: Normal
  Deposit date: 23/10/2025
  Reading date: pending
  Reading time: pending
  Reading place: pending
  Thesis director: ALIER FORMENT, MARC | MORA SERRANO, FRANCISCO JAVIER
  Thesis abstract: Learning mathematics at secondary school is difficult for many students. In the Catalan education system, national and international evaluations have shown little to no improvement in mathematical competency by the end of secondary education. Instruction design plays a critical role in how this learning happens. The central hypothesis of this thesis is that learning outcomes in secondary mathematics could be significantly better when learning tasks are sequenced from simple to complex. However, determining what makes a mathematical task complex is not straightforward and lacks academic consensus. The main goal of the thesis was to investigate precise definitions, reliable measurement approaches, and effective sequencing strategies of learning complexity in mathematics, specifically within secondary education.The research was framed under cognitive load theory, which explores the educational implications of human cognitive architecture. This architecture includes three memory systems—sensory memory, working memory, and long-term memory—that process and store information. Learning begins when information enters sensory memory, is consciously processed in working memory (which has limited capacity), and is stored in long-term memory as interconnected schemas. These schemas can later be retrieved and used without overloading working memory.Cognitive load theory proposes that the complexity of learning is determined by *element interactivity*, a central construct in the thesis. Element interactivity refers to the number of informational elements that interact or need to be managed simultaneously within a task. The greater the number of elements, the higher the cognitive demand on working memory, increasing the risk of overload and hindering learning.Using this definition, the research began with the development of the *Mathematical Knowledge Matrix* (MKM), a tool to assess the complexity of learning sequences and tasks. Initial analyses revealed limitations in the MKM, as it did not account for other relevant sources of complexity affecting mathematics learning. A literature review showed a knowledge gap regarding the range of complexity factors influencing element interactivity, prompting the need for more precise definitions of mathematical complexity. The first study addressed this by generating a taxonomy of complexity sources and examining whether element interactivity could explain all of them.Although element interactivity was a valuable construct for explaining various forms of mathematical complexity, clear guidelines for using it to assess different sources were lacking. The second study developed methods to measure complexity arising from multiple sources—providing guidelines for applying element interactivity more precisely—and tested their reliability against real students’ perceptions. Students’ perceived complexity data were also used to evaluate the relative influence of each source and to explore how these sources collectively accounted for overall task complexity.Findings from the second study showed that element interactivity arising from the *knowledge* and *operations* of tasks had the strongest influence on perceived complexity. Therefore, the third study aimed to design effective task sequencing strategies considering these two variables separately, in comparison to a control group. Both sequencing approaches improved learning outcomes, but only the *operativity-based* sequence produced statistically significant results.The investigation concluded that element interactivity is an effective construct for measuring mathematics complexity when considered from multiple sources. It can be measured to inform simple-to-complex task sequences that enhance learning. Moreover, the results from the third study suggest that disregarding operational complexity may lead to reduced learning gains, even in already proven didactic strategies.
  

DOCTORAL DEGREE IN ENVIRONMENTAL ENGINEERING

• SAVADKOOHI, MARJAN: An Advanced Control Strategy for Optimizing HVAC System Performance in Non-Residential Buildings
  
  Author: SAVADKOOHI, MARJAN
  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 ENVIRONMENTAL ENGINEERING
  Department: Department of Civil and Environmental Engineering (DECA)
  Mode: Normal
  Deposit date: 10/09/2025
  Reading date: 24/11/2025
  Reading time: 11:00
  Reading place: Place: ETSECCPBUPC, Campus NordBuilding C1. Classroom: 002C/Jordi Girona, 1-308034 Barcelona
  Thesis director: CASALS CASANOVA, MIQUEL | MACARULLA MARTÍ, MARCEL
  Thesis abstract: This PhD research addresses the scientific and practical challenges in implementing advanced control systems (ACS) for HVAC optimization in non-residential buildings. While adaptive and predictive strategies, especially model predictive control (MPC) and neural network (NN)-based methods, have shown promise in research, real-world deployment remains limited. Barriers include insufficient building historical data, technical limitations of HVAC systems, lack of building energy management systems (BEMS) standardization, and low institutional readiness. To address these, this thesis uses a dual-method approach combining empirical analysis and simulation-based experimentation.First, a survey of 676 non-residential buildings evaluates BEMS practices, focusing on HVAC control, data storage, and predictive control adoption. While smart metering and sensors are increasingly common, predictive control is reported in only 0.6% of buildings. Key barriers identified include a lack of environmental data logging, obsolete HVAC systems that do not support integration with predictive control technologies, limited technical expertise among building operators, and insufficient investment frameworks, particularly in the public sector.To respond to data and implementation challenges, the second part develops and evaluates NN-based predictive controllers using a calibrated building energy model. Eight NN models are trained on varying amounts of historical data to assess impacts on prediction accuracy and HVAC performance. Validations use consistent KPIs for thermal comfort and energy efficiency. Results show that 1–4 months of data are needed for acceptable performance, reaching a performance threshold after two years. Data preprocessing helps in data-limited cases (<100 instances), but adds little value with larger datasets, suggesting a context-specific role.Further analysis explores operational and climatic sensitivities. In cold climates and post-HVAC shutdown periods (e.g., Monday mornings), models struggle due to sparse training data. Performance improves mid-week and in warm zones, highlighting the need for diverse and climate-adapted training data. Compared to rule-based scheduling, NN controllers consistently improve energy use and comfort, especially when supported by adequate data and system configuration.This thesis offers novel insights into deploying intelligent HVAC control systems. It identifies data thresholds for effective predictive control, clarifies preprocessing roles, and provides guidance on model adaptation to climate and operations. It also highlights broader needs such as standardizing data acquisition, training energy professionals, and fostering public-private collaboration to reduce implementation risk. The findings support scalable predictive control in practice and contribute to the goals of energy efficiency, smart building management, and decarbonization.
  

DOCTORAL DEGREE IN GEOTECHNICAL ENGINEERING

• BENHAMMADI, RIMA: Convective mixing in heterogeneous porous media
  
  Author: BENHAMMADI, RIMA
  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: 22/10/2025
  Reading date: 25/11/2025
  Reading time: 11:00
  Reading place: ETSECCPB. UPC, Campus Nord Building C1. Classroom: 002 C/Jordi Girona, 1-3 08034 Barcelona
  Thesis director: DENTZ, MARCO | HIDALGO GONZÁLEZ, JUAN JOSÉ
  Thesis abstract: This thesis seeks to advance the understanding of convective mixing in heterogeneous porous media, a topic that remains comparatively underexplored compared to its homogeneous counterpart. Through the combination of high-resolution numerical simulations and laboratory experiments, we explore how spatial variability in permeability influences the onset, development, and efficiency of convective mixing processes, with applications to thermal convection, CO$_{2}$ dissolution and reactive transport.First, we begin by investigating thermal convection in the classic Horton-Rogers-Lapwood (HRL) configuration, where permeability fields are modeled as two-dimensional, log-normally distributed random fields with varying variance and correlation lengths. These serve as quantitative measures of the underlying heterogeneity. Our conducted parametric study shows that increasing the variance and/or the correlation length of the log-permeability field enhances segregation, sharpens thermal interfaces, and leads to more irregular flow structures. While the dissolution flux decreases with Rayleigh number in both homogeneous and heterogeneous systems, its sensitivity to permeability variance becomes more pronounced at longer correlation lengths. In highly heterogeneous cases, high-permeability zones near boundaries coincide with stagnation points that influence the formation of temperature plumes and localised strain rates, while the interface width decreases, indicating enhanced stretching and deformation due to the underlying structure.Next, we study CO$_{2}$ convective dissolution in heterogeneous Hele-Shaw cells, via a combined experimental-numerical approach. Heterogeneity is introduced through variations in the cell gap width, corresponding to a log-normal distribution of permeability with fixed variance and correlation lengths. Results show that heterogeneity advances the onset of instability, increases the amplitude and growth rate of convective fingers, and causes more distorted and dispersive flow patterns. However, the dimensionless wavenumber of the instability remains similar to that in homogeneous cells. A comparison of the autocorrelation functions of the fingering patterns and the permeability field shows that heterogeneity increases the dimensionless correlation length of the fingering pattern, which in turn slows down its growth once the finger size becomes comparable to the heterogeneity scale.Finally, we investigate reactive convective dissolution involving the bimolecular chemical reaction \( \A + \B \rightarrow \C \), across four permeability configurations: homogeneous, horizontally layered, vertically layered, and multi-Gaussian log-normally distributed fields. Key metrics such as product mass, reaction rate, front position and width and mixing length are all substantially affected by the structure of the permeability field. Vertically layered and log-normal configurations promote more efficient mixing and faster front progression. Overall, when horizontal correlation length is small relative to the vertical, convective transport and mixing efficiency are maximised.Collectively, these findings demonstrate that it is not simply the presence of heterogeneity, but the specific structure of the permeability, particularly its variance and spatial correlation, that fundamentally governs convective behaviour. The insights gained show the necessity of incorporating geologically realistic heterogeneity into the predictive models.
  
• DAWI, MALIK ALI A: Process-Based Numerical Models to Assess Hydrogeochemical Effects of Microbial Biofilms in Porous Media
  
  Author: DAWI, MALIK ALI A
  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: 16/10/2025
  Reading date: pending
  Reading time: pending
  Reading place: pending
  Thesis director: SANCHEZ VILA, FRANCISCO JAVIER | STARNONI, MICHELE
  Thesis abstract: Microorganism presence and spatial distribution over time in natural porous media, such as soils, sediments, and aquifers, play critical roles in mediating geochemical processes, influencing contaminant fate, and maintaining ecosystem functionality. In particular, microbial communities in the form of biofilms mediate complex biotransformation reactions, significantly altering the hydraulic properties of the host porous system. These dynamics, coupled with multiscale physical and chemical interactions, present major challenges for the predictive modeling of microbial processes in porous media. This thesis aims at developing a suite of computational models that integrate microbial growth and activity within flow and transport frameworks. The work is structured around three main contributions. First, a hybrid pore-scale model is developed which couples a micro-continuum representation of biofilms with a particle-based transport solver, enabling detailed analysis of how biofilm morphology and structure influence conservative solute transport. Second, this framework is extended to simulate the dynamic biofilm development and its interaction with groundwater flow, introducing a cohesive microporous model for biofilms that incorporates growth, attachment, spreading, and flow-induced detachment. A novel dimensionless number is introduced to characterize the interplay between hydrodynamic forces and biofilm cohesion. Third, we revisit Monod kinetics by proposing a mechanistic two-step reaction scheme that linearizes the growth rate expression, facilitating its integration into particle-based reactive transport models. This formulation is validated against batch experiments and applied to simulate microbial degradation in porous media. Finally, the thesis synthesizes these findings and outlines future directions for model development and experimental integration. By combining theoretical insights with computational advances, this work contributes to a deeper understanding of microbially mediated processes in porous media and provides modeling tools to support both hypothesis testing and experimental research in environmental and engineered systems.
  
• SAYAD NOGHRETAB, BABAK: HYDRO-MECHANICAL MODELING OF GAS FLOW THROUGH CLAY-BASED ENGINEERED ISOLATION BARRIERS
  
  Author: SAYAD NOGHRETAB, BABAK
  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: 16/10/2025
  Reading date: pending
  Reading time: pending
  Reading place: pending
  Thesis director: PUIG DAMIANS, IVAN | OLIVELLA PASTALLE, SEBASTIAN
  Thesis abstract: Safe management of high-level radioactive waste (HLRW) requires durable isolation from the biosphere over geologic time. Deep geological repositories (DGRs) rely on engineered and natural barriers, with bentonite as a key buffer and backfill material because it seals fractures, sorbs radionuclides, and develops swelling pressure during hydration. During operation and early post closure, resaturation and corrosion generate gas, so predicting system behavior requires coupled hydro gas mechanical models that represent double porosity, heterogeneity, and preferential pathways. This Thesis addresses that need by integrating explicit pathway mechanics in compacted buffers, double porosity constitutive laws for pellet/powder mixtures, and image-based statistics linked to finite element simulations in CODE_BRIGHT.First, a three-dimensional coupled hydro gas mechanical model of the large-scale gas injection test (LASGIT) is formulated with heterogeneous initial permeability, embedded fractures with dilatancy, and explicit gap closure states at the canister–buffer interface and is exercised through targeted sensitivity analyses. Second, the BENTOGAZ laboratory mixture of equal parts pellets and MX-80 powder is modeled with the Barcelona Expansive Model to couple microstructure and macrostructure; systematic parameter studies are complemented by a handmade heterogeneity setup that assigns distinct properties to randomly distributed pellet and powder domains. Third, an image to model workflow for SEALEX links micro-CT analysis to simulation: binarized slices yield macroporosity maps, directional variograms quantify anisotropy and correlation lengths, and the fitted statistics generate anisotropic porosity fields that enable automatic heterogeneity on the finite element mesh.Together, these methodologies constitute a set of methods that couple explicit fractures with dilatancy, dual structure behavior, and image informed spatial heterogeneity for repository relevant assessment of gas entry, resaturation, and sealing performance.
  
• YAZDANI CHERATI, DAVOOD: Hydromechanical Simulation of Argillaceous Rocks for Radioactive Waste Disposal Applications
  
  Author: YAZDANI CHERATI, DAVOOD
  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: 28/07/2025
  Reading date: 14/11/2025
  Reading time: 12:00
  Reading place: ETSECCPB. UPC, Campus NordBuilding C1. Classroom: 002C/Jordi Girona, 1-308034 Barcelona
  Thesis director: VAUNAT, JEAN | GENS SOLE, ANTONIO
  Thesis abstract: Argillaceous claystones are primarily composed of clay particles of sedimentary origin andcontain a substantial amount of chemically precipitated cement, often calcium carbonate, whichacts as a bonding agent. Due to their favorable properties—such as low permeability, minimalmolecular diffusion, self-sealing capabilities, and high retention capacity for radionuclides—theyare considered suitable host geomaterials for the deep geological disposal of radioactive waste.However, fractures within these geomaterials, induced by excavations or post-disposal processes,can create preferential pathways for radionuclide migration, potentially influencing theperformance of the disposal system. Therefore, these problems should be numerically evaluated.However, due to their complex behavior, modeling argillaceous rocks presents a significantchallenge. Under shearing, these geomaterials exhibit anisotropy, creep, and quasi-brittle failurecharacterized by significant post-peak softening and strain localization. This study aims toinvestigate the hydromechanical response of Callovo-Oxfordian (COx) argillaceous claystones tolaboratory tests, field excavations, and post-disposal processes by employing the argillite modelsimplemented in the CODE-BRIGHT program. The argillite models are adopted since they caneffectively reproduce the key characteristics of argillaceous materials. Additionally, throughoutthis thesis, several other constitutive models are applied to simulate the behavior of materialsinteracting with the COx, including soft and rigid supports, and swelling materials. The outcomesof this thesis provide significant insight into the hydromechanical behavior of argillaceous rocks,thereby contributing to a more accurate evaluation of the disposal process.
  

DOCTORAL DEGREE IN MARINE SCIENCES

• ANGELINI, RICCARDO: Coastal environment monitoring through satellite, terrestrial and airborne remote sensing
  
  Author: ANGELINI, RICCARDO
  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 MARINE SCIENCES
  Department: Department of Civil and Environmental Engineering (DECA)
  Mode: Normal
  Deposit date: 01/10/2025
  Reading date: 24/11/2025
  Reading time: 10:30
  Reading place: Civil and Environmental Engineering Department, University of FlorenceVia di Santa Marta 3, Florence
  Thesis director: MASIERO, ANDREA | LUZI, GUIDO | RIBAS PRATS, FRANCESCA
  Thesis abstract: Coastal areas are increasingly threatened by sea-level rise due to climate change and anthropogenic pressures, calling for robust and scalable monitoring tools. The first phase of this thesis implements a comprehensive methodology for semi-automatic shoreline extraction through the use of multispectral satellite imagery (Sentinel-2 and PlanetScope). The extracted shorelines are validated using in situ GNSS surveys and high-resolution orthomosaics along three Mediterranean sandy beaches. The shoreline extraction tool works with several spectral indices tested with thresholding and unsupervised clustering segmentation methods. A high coastline extraction performance is achieved using Sentinel-2, with an average sub-pixel accuracy of 4 m (Mean Absolute Deviation, MAD) obtained from a 10 m pixel. A MAD of 2 m is achieved from imagery at 3 m pixel resolution of PlanetScope. In the second phase, the obtained multispectral satellite shorelines are used to characterize megacusps shoreline undulations with alongshore wavelengths of hundreds of meters and cross-shore amplitudes up to a few tens of meters that can significantly affect beach usability. Subsequent validation with reference data proves satellite-derived shorelines can robustly and accurately describe megacusp parameters such as amplitude and wavelength. Moreover, megacusp evolution can be effectively characterized by combining different types of satellite imagery (Sentinel-2 and PlanetScope), enabling the identification of periods of growth, decay, and migration, even at weekly timescales. This can be a useful tool to manage the impact of these features on Mediterranean beaches.Another phase involves evaluating and correcting the extracted shorelines to tide excursions and wave setup. Corrections based on tide gauge and buoy data show that although absolute displacements are limited, assessing them helps eliminate a potential source of error, justifying their integration into high-accuracy workflows. This would also allow applying the developed methodology to meso- or macro-tidal beaches.Finally, the research incorporates Synthetic Aperture Radar (SAR) imagery (Sentinel-1 and TerraSAR-X) to expand the shoreline extraction tool to periods without light or with clouds, including two other Mediterranean beaches characterized by gravel sediment. The SAR-based shoreline extraction module includes advanced extra-denoising filtering and an outlier detection module, but it maintains the core methodology used in the first phase, demonstrating the flexibility of the developed approach. The results demonstrate unprecedented accuracy and stability for gravel beaches (approximately 6 m of MAD), and for sandy beaches (approximately 7 m of MAD), starting from a 10 m pixel size. In a comparative assessment between the two TerraSAR-X images available and the closest Sentinel-1 in terms of time, the first achieves higher accuracy in terms of MAD (2.5 m) compared to the second one (6.5 m), but only in the image with good meteorological conditions (no differences are obtained in the other date). The study also investigates the influence of radar parameters, such as polarization, wavelength, acquisition geometry, and environmental conditions, on SAR shoreline accuracy. The results show that higher land–sea backscatter contrast, typical of gravel beaches, and moderate wind conditions enhance detection reliability. Conversely, high wind and wave activity reduce contrast and increase errors.Overall, this work offers robust, scalable tools for high-resolution coastal morphology monitoring in Mediterranean beaches using satellite data. These methodologies could be extended to beaches with significant tides and other related fields such as flood mapping. The developed algorithms could be incorporated into operational platforms like early warning systems or interactive WebGIS applications, potentially significantly contributing to local authorities' adaptive coastal zone management.
  

DOCTORAL DEGREE IN MATERIALS SCIENCE AND ENGINEERING

• AL ZEYADI, NOORA TALIB MOHAMMED: 3D printing of aluminum alloys under different extrusion techniques
  
  Author: AL ZEYADI, NOORA TALIB MOHAMMED
  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 MATERIALS SCIENCE AND ENGINEERING
  Department: Department of Materials Science and Engineering (CEM)
  Mode: Normal
  Deposit date: 18/07/2025
  Reading date: 17/11/2025
  Reading time: 11:30
  Reading place: UPC Facultat de Matemàtiques i Estadística Campus Diagonal Sud, Carrer de Pau Gargallo, 14, Distrito de Les Corts, 08028 Barcelona SALA D'ACTES
  Thesis director: CABRERA MARRERO, JOSE MARIA | FENOLLOSA ARTÉS, FELIP
  Thesis abstract: This doctoral research evaluated the feasibility of using various aluminum-based feedstocks in additive manufacturing (AM) to develop cost-effective and environmentally friendly alternatives to traditional metal fabrication. The study systematically examined AA6061 filament, AlSi10Mg granules (commercial and recycled), and AlSi10Mg powder paste across three AM techniques: Fused Deposition Modeling (FDM), screw-based extrusion, and Direct Ink Writing (DIW). The main objectives were to optimize printing, thermal debinding, and sintering parameters for each feedstock and AM technique, and to assess the resulting mechanical properties and microstructures of the fabricated parts.For printing, AA6061 filament processed via FDM achieved optimal results with a 0.8 mm nozzle diameter at 205 °C. AlSi10Mg granules (commercial and recycled) and AlSi10Mg powder paste, used in screw-based extrusion and DIW respectively, performed best with 0.6 mm nozzles and lower temperatures. These optimizations established critical baseline conditions for subsequent processing steps, emphasizing the distinct requirements of each material and technique.Thermal debinding, essential for removing polymeric binders before sintering, was optimized for each feedstock. For AA6061 filament, 550 °C with holding times up to 3 hours was most effective. For commercial AlSi10Mg granules, 350 °C for 3 hours yielded optimal results, a condition that also worked for recycled granules and powder paste. These parameters minimized defects and prepared the parts for successful sintering.Sintering parameters were rigorously optimized to ensure densification and desired mechanical properties. AA6061 filament was best sintered at 635 °C, while commercial AlSi10Mg granules and powder paste achieved optimal results at 600 °C. Recycled AlSi10Mg granules reached peak performance at 620 °C. All sintering was conducted for 3 hours under a nitrogen atmosphere with vacuum and oxygen traps. SEM analysis confirmed increased densification and uniform microstructures under these conditions.A pre-sintering pressing technique was introduced to further enhance densification and reduce porosity. This step significantly improved the relative density of sintered parts by 19.25–45.55%, with pressed samples achieving densities up to 93.65%. Mechanical testing showed that recycled AlSi10Mg granules provided the highest compressive strength (168.34 MPa), followed by commercial granules, AA6061 filament, and powder paste.
  
• ORTIZ MEMBRADO, LAIA: Nanoindentation mapping of multiphase materials: statistical analysis and machine learning approaches
  
  Author: ORTIZ MEMBRADO, LAIA
  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 MATERIALS SCIENCE AND ENGINEERING
  Department: Department of Materials Science and Engineering (CEM)
  Mode: Normal
  Deposit date: 21/10/2025
  Reading date: pending
  Reading time: pending
  Reading place: pending
  Thesis director: JIMENEZ PIQUÉ, EMILIO | MATEO GARCIA, ANTONIO MANUEL
  Thesis abstract: This doctoral thesis focuses on the micromechanical characterization of multiphase materials through high-speed nanoindentation mapping (HSNM), combined with statistical and machine learning techniques. The aim is to extract and interpret mechanical properties with spatial resolution from large datasets, improving the understanding of microstructure-property relationships in complex systems such as ceramic-metal composites and heterogeneous steels.HSNM enables the acquisition of localized data with micrometric resolution over large areas, but its use presents several challenges: optimizing indentation spacing, interpreting scattered data, limitations of Gaussian distributions in representing micromechanical properties, and difficulties in classifying regions near interfaces. Moreover, there is growing interest in automating interpretation using machine learning.The objectives of this thesis include: (i) assessing industrially relevant materials with HSNM, (ii) applying unsupervised learning to quantify micromechanical transitions, (iii) introducing skewed distributions as alternatives to Gaussian fitting, and (iv) developing supervised models to classify nanoindentation responses based on the full curve shape.Methodologically, the thesis implements Gaussian Mixture Models (GMM) to cluster mechanical properties and identify phases in materials such as WC-Co and superduplex steels. This strategy allows for wide-area surface analysis and the detection of mechanical transitions, such as hardening gradients in advanced high-strength steels (AHSS) and property changes induced by electron beam melting (PBF-EB) in 316L/V4E alloys.To address asymmetric or dispersed data, Skew-normal distribution fitting is introduced, offering a more faithful representation of reality, especially in interface-influenced regions like hardmetals. This approach improves phase classification compared to traditional Gaussian fits.The thesis also develops a supervised model based on convolutional neural networks (CNNs), trained with mechanical response curves transformed into two-dimensional images that preserve their shape. This model enables accurate classification of responses into known phases and provides a continuous confidence score for each classification. This represents a paradigm shift toward similarity-based classification, facilitating the construction of continuous maps capable of realistically capturing micromechanical transitions and interfacial behavior.Overall, this work demonstrates the potential of HSNM combined with statistical and machine learning methods for characterizing complex multiphase materials. The thesis opens new pathways for improving the interpretation of heterogeneous mechanical behavior and integrating it with microstructural data, contributing to the development of more robust and automated methodologies in materials science.
  

DOCTORAL DEGREE IN MECHANICAL, FLUIDS AND AEROSPACE ENGINEERING

• MASSAROTTI, GIORGIO PAOLO: New Dual Steering System in a Compact Tractor
  
  Author: MASSAROTTI, GIORGIO PAOLO
  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 MECHANICAL, FLUIDS AND AEROSPACE ENGINEERING
  Department: Department of Mechanical Engineering (EM)
  Mode: Normal
  Deposit date: 01/10/2025
  Reading date: 28/11/2025
  Reading time: 10:00
  Reading place: Sala de Juntes, Ed. TR5, ESEIAAT
  Thesis director: GAMEZ MONTERO, PEDRO JAVIER | CODINA MACIA, ESTEBAN
  Thesis abstract: In order to achieve optimal controllability in a dual-steering tractor (a four-wheel, iso-diametric tractor equipped with a dual-hydraulic steering system), this thesis proposes a coordinated approach that combines experimental testing (using a special agricultural tractor) with numerical analysis of the entire vehicle, developed in Bond Graph-3D. After an exhaustive review of the scientific literature, it is observed that the compact tractor with dual steering, has not yet been thoroughly analysed. In this thesis, in chapter 1 it is possible to identify the reasons that led to the realization of this long work and the objectives that were set at the beginning. These objectives were born from the understanding of the state of the art relating to double steering in the off-road sector, focusing particularly on the case of a vineyard tractor. All starting from the basics, from the steering which occurs smoothly and through the variants that can be found on the market today. In light of the machine construction information, the model of the studied tractor was introduced, searching the literature for the methods and models that could describe its dynamic behavior. In order to detail the description, the hydraulic circuit chosen based on the requirements listed in chapter 4 was introduced and an analysis to its modeling combined with the dynamic model of the tractor using Bond-Graph was provided. At the same time, experimental tests were carried out with a prototype tractor which incorporated the hydraulic circuitpreviously described, together with the dynamic model of the tractor, also obtained through modeling from the physical machine. The numerical analysis provided results that match very well with the experimental data, providing the key to the "salient" factors that characterize the tractor's steering capacity. A threshold can be set, relative to the vehicle speed, to disable dual-steer mode when a certain speed is exceeded. Based on experimental data, this threshold is set around 8.5 km/h. In conclusion, there are possibilities for future development that would lead the system described to a new circuit capable of appreciating not only the factors that determine the drivability of the tractor, but also of managing possible dangerous conditions for the user.
  

DOCTORAL DEGREE IN NETWORK ENGINEERING

• PALOMARES TORRECILLA, JAVIER: Enabling collaborative Intelligence in Heterogeneous Edge to Cloud Continuum.
  
  Author: PALOMARES TORRECILLA, 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 NETWORK ENGINEERING
  Department: Department of Network Engineering (ENTEL)
  Mode: Normal
  Deposit date: 21/10/2025
  Reading date: pending
  Reading time: pending
  Reading place: pending
  Thesis director: CERVELLO PASTOR, CRISTINA | CORONADO CALERO, ESTEFANÍA | SIDDIQUI, MUHAMMAD SHUAIB
  Thesis abstract: The growing convergence of the Industrial Internet of Things (IIoT), Artificial Intelligence (AI), and Multi-access Edge Computing (MEC) is reshaping service orchestration across the Edge-to-Cloud Continuum. These paradigms enable intelligent and flexible applications in heterogeneous, resource-constrained environments. A representative use case is multi-Automated Guided Vehicle (AGV) systems in smart factories, where moving agents must make time-sensitive decisions while adapting to variable connectivity, shifting computational loads, and coordinating with remote control logic. These scenarios demand orchestration mechanisms that dynamically integrate mobility, computation, and communication across infrastructure tiers. However, such systems exhibit highly dynamic behavior, driven by factors like device mobility and fluctuating resource availability, which creates significant challenges for scalable, fair, and autonomous orchestration under strict service guarantees.To address these demands, this thesis proposes a novel architecture unifying Information Technology and Operational Technology, extending MEC orchestration to the factory floor. It introduces an enhanced MEC orchestrator (MEO) and extended service descriptors to capture hardware-aware, location-sensitive, and non-computational constraints, enabling precise, constraint-driven placement across the Continuum. The approach is validated through a multi-AGV coordination use case.To optimize service placement, two orchestration mechanisms based on Deep Reinforcement Learning (DRL) are introduced. The first, a DRL-based Multi-Task Scheduling (DRL-MTS) strategy, minimizes end-to-end delay by distributing tasks across nodes while respecting resource availability. The second, the Intelligent Placement Algorithm (IPA), enables hardware-aware deployment by accounting for resource granularity and spatial constraints. Both integrate context-aware scheduling for adaptive and efficient placement.For decentralized coordination, this thesis proposes multi-agent learning strategies. The Multi-Agent Collaborative Protocol for Dynamic Resource Allocation (MACP-DRA) uses DRL to ensure fair and efficient placement under competition and resource constraints. The Multi-Agent Dynamic Bandwidth Environment (MADBE) manages bandwidth across heterogeneous service tiers with diverse communication demands, enabling real-time adaptation through policy specialization and aggregation. To further reduce bottlenecks in collaborative training, an Explicit Congestion Notification–based gradient compression mechanism for federated learning lowers bandwidth overhead while preserving accuracy. Collectively, these contributions enhance adaptability, communication efficiency, and fairness, enabling scalable and robust coordination across the Continuum.This thesis employs system modeling, algorithm design, and simulation-driven evaluation in realistic industrial scenarios with dynamic workloads, agent competition, and variable networks. The framework outperforms state-of-the-art baselines in latency, bandwidth, fairness, and SLA compliance. Its modular architecture and enriched descriptors enable constraint-aware deployment at the industrial edge, with the MEO supporting federated and cross-domain coordination. DRL mechanisms improve delay-sensitive scheduling, while multi-agent strategies ensure fairness and resilience. Validated through large-scale simulations and an emulated multi-AGV testbed, the approach achieves higher placement success, lower latency, better resource use, and reduced conflicts, establishing a foundation for research on distributed decision-making, cross-domain orchestration, and AI-driven automation.
  

DOCTORAL DEGREE IN NUCLEAR AND IONISING RADIATION ENGINEERING

• MONT I GELI, NIL: Characterization of underground neutron fluxes by experimental measurements, Monte Carlo simulations and improvement of (α,n) nuclear data
  
  Author: MONT I GELI, NIL
  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 NUCLEAR AND IONISING RADIATION ENGINEERING
  Department: Department of Physics (FIS)
  Mode: Normal
  Deposit date: 30/09/2025
  Reading date: 17/11/2025
  Reading time: 12:30
  Reading place: Secció d'Enginyeria Nuclear, pavelló C, planta 1, Aula del Màster (porta 31.07)
  Thesis director: CALVIÑO TAVARES, FRANCISCO | TARIFEÑO SALDIVIA, ARIEL
  Thesis abstract: particularwith the radiation detectors used in scientific research, resulting in a radiationbackground that could become one of the main limiting factors that determines thefeasibility of the measurement. To overcome such a limitation, rare event experimentsin fields such as neutrino physics, direct searches of dark matter, and nuclear astrophysicshave to be carried out in underground laboratories. The rock overburdenshields most of the cosmic radiation, but experiments still require carefully designedradiation shielding to avoid the impact from the underground radiation. This radiationcomes mainly from (α,n) reactions and spontaneous fission processes caused bythe intrinsic radioactivity of the walls of the laboratory. As a consequence, a precisecharacterization of the underground radiation fluxes is crucial for many experiments.Neutrons are one of the main radiation types that affect underground experiments.The High Efficiency Neutron Spectrometry Array (HENSA) is a high-efficiency neutrondetection system that was designed to characterize the neutron flux in undergroundlaboratories. Today, the spectrometer has been used in facilities suchas the Canfranc Underground Laboratory (LSC) in Spain, the underground facilityFelsenkeller in Germany, and the Gran Sasso National Laboratory (LNGS) in Italy.This thesis is focused on the characterization of the neutron flux at LSC, in particularin hall B of the facility named LAB2400. To do that, experimental measurementswith HENSA are combined with Monte Carlo calculations. For more than four years,neutron data were acquired in the same location within hall B. It was found thatthe neutron flux remained stable during the whole measurement, with any possiblefluctuation being smaller than the monthly resolution of the spectrometer. The resultsof HENSA, in particular the magnitude and energy distribution of the neutronflux were also used to asses the impact of such neutrons on the background of theANAIS-112 dark matter experiment, which is located close to the setup of HENSA.In collaboration with the ANAIS team, the impact of underground neutrons on thebackground of ANAIS-112 was found to be negligible. Furthermore, the impact of(α,n) data on Monte Carlo calculations of the underground neutron flux was studied.Recent technical meetings organized by the International Atomic Energy Agency(IAEA) have concluded that there is a need to improve nuclear data on (α,n) reactions.Such reactions are of primary interest not only in underground physics but also infields such as nuclear astrophysics, medical physics, technologies, and non-destructiveassays for spent fuel management applications. To improve the status of the nucleardata, the Measurement of Alpha Neutron Yields and spectra (MANY) collaborationwas formed. The project is based on the use of the currently existing infrastructurein Spain, in particular the α beams produced by the accelerators at CMAM (Centrode Micro-An´alisis de Materiales) and CNA (Centro Nacional de Aceleradores).In the context of the MANY collaboration, the final part of the present thesis dealswith the design and development of a new moderated neutron counter, miniBELEN,to perform measurements of (α,n) production yields and reaction cross sections. Theanalysis of the commissioning measurement using aluminum targets is also discussed.The results obtained are consistent with the existing data in the literature, showingthat miniBELEN is able to perform measurements of (α,n) yields and cross sections.The measurement also produced new data for the cross section of 27Al(α,n)30P foralpha energies greater than 5.5 MeV.
  
• PALLÀS I SOLÍS, MAX: Study of neutron-rich β-delayed emitters relevant for understanding the formation of the r-process rare earth-peak around mass A~160
  
  Author: PALLÀS I SOLÍS, MAX
  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 NUCLEAR AND IONISING RADIATION ENGINEERING
  Department: Department of Physics (FIS)
  Mode: Normal
  Deposit date: 30/09/2025
  Reading date: 14/11/2025
  Reading time: 11:00
  Reading place: Secció d'Enginyeria Nuclear, pavelló C, planta 1, Aula del Màster (porta 31.07)
  Thesis director: TARIFEÑO SALDIVIA, ARIEL | TOLOSA DELGADO, ALVARO
  Thesis abstract: The r-process is responsible for the formation of nearly half of all nuclei heavier than iron. New elements are synthesized via the r-process, involving neutron-rich nuclei characterized by the emission of neutrons following beta decay. Precise network computations are crucial for understanding the astrophysical conditions of the r-process and replicating observed abundance distributions. These calculations rely heavily on data regarding nuclear structure, often based on theoretical estimates for isotopes that are not experimentally accessible. High-precision experimental data for isotopes far from stability play a crucial role in refining nuclear structure models, which, in turn, enhance the reliability of r-process network calculations.In this context, the beta-delayed neutrons at RIKE (BRIKEN) collaboration performed high-precision measurements of the half-lives and neutron emission probabilities of neutron-rich nuclides. The setup consisted of the Advanced Implantation Detector Array (AIDA) placed inside the BRIKEN neutron counter, an array of 3He neutron counters embedded in a polyethylene matrix. The experiment was performed in the Rare Isotope Beam Factory (RIBF) of the RIKEN Nishina Center (Japan).The present thesis is centered on the analysis of the BRIKEN RIBF-148 experiment, specifically for nuclei spanning the range from 146Ba to 162Nd; these are pivotal in the r-process nucleosynthesis of rare-earth elements. The findings of this research include 36 T1/2 values that are consistent with earlier experimental data. Of these, 13 measurements also provide a reduced uncertainty compared to previously reported values. For the P1n study, it was the first experiment in this region, with 22 of the 24 values being new measurements.Additionally, we developed a revised RHB+RQRPA nuclear structure model, incorporating several improvements. The model’s predictive capabilities were enhanced by refining its parameterization with the experimental data presented in this thesis.In the final part of this work, we discussed the preliminary astrophysical impact of these new experimental data and the refined nuclear model on r-process abundances in the REP region through nuclear reaction network calculations.
  

DOCTORAL DEGREE IN PHOTONICS

• CHIEN, YING-HAO: Revealing Ultrafast Dynamics in Hexagonal Boron Nitride with Attosecond X-ray Absorption Fine-structure Spectroscopy
  
  Author: CHIEN, YING-HAO
  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: 16/10/2025
  Reading date: pending
  Reading time: pending
  Reading place: pending
  Thesis director: BIEGERT, JENS
  Thesis abstract: Since the invention of the integrated circuit (IC) in the 1950s, modern civilization has been built upon its foundation. As ICs continue to scale down and operate at higher speeds, managing heat dissipation and energy transfer process is critical to overcoming performance limitations and enabling the development of next-generation ICs. In classical models, electrons and phonons are treated as independent systems to simplify calculations. This approximation successfully describes electronic band structures, charge transport, and optical responses in many materials under equilibrium conditions. However, it neglects the critical role of electron-phonon coupling, a fundamental many-body interaction that governs non-equilibrium energy exchange between electronic and lattice degrees of freedom. Recent advances in attosecond X-ray absorption fine structure (atto-XAFS) spectroscopy offer an unprecedented opportunity to observe electron-phonon coupling dynamics with both attosecond temporal and element-specific resolution. Hexagonal boron nitride (hBN), a widely studied prototypical material with diverse applications, still presents unresolved questions regarding its ultrafast dynamics. In this work, we investigate the coupled electron and phonon dynamics in bulk hBN using atto-XAFS. By employing different excitation conditions and exploiting different temporal resolutions, we disentangle the respective contributions of electrons and phonons to the transient response, demonstrating the unique capability of atto-XAFS to probe many-body dynamics in real-time. To enable further studies of novel materials, we upgraded our titanium-doped sapphire (Ti:sapphire) chirped pulse amplification (CPA) laser system, integrated a new commercial TOPAS optical parametric amplifier, designed a novel microfluidics gas target combined with a piezo pulse valve system aimed at reducing helium consumption for high harmonic generation (HHG), implemented a cryogenic sample mount for temperature-dependent measurements, and replaced the diffraction grating in the soft X-ray spectrograph with high diffraction efficiency and high resolving power reflection zone plates. We demonstrate the enhanced performance of the upgraded system for future advanced atto-XAFS experiments.
  
• KOKABEE, OMID: High-power ultrafast optical parametric oscillators from the visible to mid-infrared
  
  Author: KOKABEE, OMID
  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: 09/07/2025
  Reading date: 11/11/2025
  Reading time: 10:00
  Reading place: ICFO Auditorium
  Thesis director: EBRAHIM-ZADEH, MAJID
  Thesis abstract: The introduction of electric lighting in Architecture marked a profound transformation in its design conception, establishing artificial light as a fundamental element in the configuration of space. Unlike other artistic and architectural disciplines, artificial architectural lighting lacks a formalised Art History. Existing specialist literature remains largely focused on technical and quantitative aspects, frequently relegating the qualitative dimensions of light in space to a secondary status. Consequently, there is a notable absence of a specific vocabulary capable of accurately describing the qualitative effects of lighting in architecture. This lexical gap hampers the effective communication of lighting-related spatial concepts, ultimately to the detriment of architectural practice. In light of these challenges, and with the aim of improving both design and pedagogical methodologies, this research advocates for the establishment of a dedicated vocabulary for qualitative architectural lighting. It is predicated on the hypothesis that it is feasible to construct a consensual glossary that enables the precise articulation of the formal and spatial attributes of lighting effects within architectural environments. To substantiate this hypothesis, the research sets out two principal objectives: first, to identify the parameters that define the qualitative aspects of lighting and to compile the associated terminological corpus; second, to develop a lexical and visual dictionary in which each term is clearly defined and illustrated, thereby facilitating its comprehension and application in both academic and professional contexts, and contributing to the standardisation of a specific and practical language.The study adopts a qualitative methodological framework, centred on the linguistic analysis of texts describing architectural lighting projects, which have been published in specialised Spanish-language media. A rigorous, systematic, and replicable terminology methodology has been employed, drawing upon established principles from the field of Terminology studies and related research on lighting perception. The process integrates automated term extraction methods, enabling efficient handling of large data sets, and applies linguistic techniques adapted to the visual domain. The research identifies the principal parameters defining the formal qualities of architectural lighting as direction, colour, and distribution, followed by quantity, luminance, sources, informational content, perceptual effects, and others. Among these, the distribution parameter emerges as the most frequently cited and, thus, the most critical for both configuring and describing architectural lighting. Accordingly, the dictionary focuses on the most recurrent terms related to distribution, listed alphabetically as follows: accent lighting, ambient lighting, composed lighting, diffuse lighting, direct lighting, directed lighting, dispersed lighting, focalized lighting, general lighting, grazing lighting, homogeneous lighting, horizontal lighting, indirect lighting, integrated lighting, precise lighting, projected lighting, reflected lighting, uniform lighting, and vertical lighting. It has been demonstrated that each of these terms can be defined in a manner that supports clear, precise, and intelligible communication within architectural lighting discourse. Furthermore, it is feasible to identify corresponding visual representations that exemplify each definition, reinforcing their pedagogical and practical applicability. In conclusion, this research affirms the viability of developing a consensual glossary of terms to imporve the communication of the formal and spatial characteristics of lighting effects within architectural practice, which constitutes a foundational step toward the recognition and standardisation of qualitative lighting vocabulary in the discipline.
  
• MORALES CURIEL, LUIS FELIPE: Deep-learning enhanced bioluminescence microscopy
  
  Author: MORALES CURIEL, LUIS FELIPE
  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: 16/10/2025
  Reading date: pending
  Reading time: pending
  Reading place: pending
  Thesis director: KRIEG, MICHAEL
  Thesis abstract: Bioluminescence microscopy presents a powerful alternative to fluorescence imaging by eliminating the need for external illumination, thereby avoiding issues such as phototoxicity, photobleaching, and background autofluorescence. However, the inherently low photon output of luciferase-based reporters significantly restricts the signal-to-noise ratio (SNR), as well as the achievable spatial and temporal resolution—challenges that are especially pronounced in dynamic or volumetric biological imaging. This thesis addresses these limitations by introducing a deep learning-driven imaging pipeline designed to enhance bioluminescence microscopy at both the data acquisition and image reconstruction stages.Our strategy integrates optical system design with advanced neural networks to enable rapid, high-resolution 3D imaging under extremely low-light conditions. We engineered a custom microscope featuring a highly compact optical axis and paired it with a single-photon sensitive camera, significantly boosting the SNR of bioluminescent images. To achieve fast volumetric imaging, we incorporated light field microscopy (LFM) and Fourier light field microscopy (FLFM), enabling single-shot 3D acquisition while improving axial and lateral resolution via Fourier-domain filtering. The primary objective of this work is to demonstrate how deep learning can substantially enhance bioluminescence microscopy, pushing the technique beyond its traditional limits in both 2D and 3D imaging.At the core of our approach is a suite of convolutional neural networks specifically trained on bioluminescent data. Using both synthetic and experimental datasets, we designed and trained models capable of extracting meaningful information from low-SNR raw data, recovering otherwise lost details and offering deeper insight into the biological sample. The models developed in this thesis cover key tasks such as denoising and reconstruction of wide-field, light field, and Fourier light field bioluminescent images. Together, they form a modular, learnable pipeline that significantly elevates the performance of bioluminescence microscopy in terms of both quality and speed.We validate our system using live biological samples, including Caenorhabditis elegans, mouse stem cells, and zebrafish embryos, capturing neuronal activity and intracellular dynamics at subsecond timescales. By placing deep learning at the heart of the imaging process, this work establishes a new paradigm for bioluminescence microscopy, transforming a traditionally low-SNR modality into a robust tool for fast, high-resolution, and label-specific imaging in living organisms.
  

DOCTORAL DEGREE IN POLYMERS AND BIOPOLYMERS

• CASADO GÓMEZ, JAIME: 3D Printable Hybrid Acrylate-Epoxy Vitrimer Resins with Improved Compatibility and Reprocessability
  
  Author: CASADO GÓMEZ, JAIME
  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: 21/10/2025
  Reading date: pending
  Reading time: pending
  Reading place: pending
  Thesis director: FERNANDEZ FRANCOS, XAVIER | KONURAY, ALI OSMAN
  Thesis abstract: The Covalent Adaptable Networks (CANs) made of polymeric materials that use dynamic covalent chemistry, allowing bonds to break and reform when stimulated, combine the mechanical properties of thermoset polymers with the ability to be reprocessed and recycled. The integration of 3D printing technology with CANs represents a significant advancement in the field of manufacturing polymer components. This innovative process offers the functional benefits of a thermoset along with the recycling advantages of a thermoplastic, making it a highly sustainable solution.In the following collection of articles, a group of novel dual-curing thermosetting materials have been designed, optimised and improved regarding their compatibility and reprocessability. In the first article, we successfully crafted four genuine resins and explored how their behaviour and properties were influenced by the unique combinations and proportions of their formulation ingredients. The original dual-curing system was performed by means of homogenously mixing an epoxy resin with a di-acrylate monomer rich in β-hydroxy ester and hydroxyls, a dicarboxylic acid and a coupling agent in a fixed proportion. The use of different transesterification catalysts in varying proportions, a methacrylate monomer and a photoinitiator round off the formulation. The combination of these chemicals results in the formation of a hybrid network, which is capable of undergoing transesterifications reactions. The 3D-printed and fully-cured parts from these four innovative resins have proven that their thermo-mechanical properties are in line with the designed specifications. Their repair and recycle capabilities are facilitated by a CAN structure.In the second article, we have optimised the formulations of 3D printable vitrimer resins with the objective of enhancing their processing, mechanical properties, and repairability/reprocessability. An improvement of the formulation was achieved through the determination of the optimal quantities of acrylates and coupling agent. A selection of epoxy resins was also made with the aim of identifying the best performing option. The resins developed in this part of the research have offered a more suitable viscosity for handling in the 3D printer. It has been demonstrated as well that parts printed from these improved resins and subsequently double-cured have shown an enhancement in their thermo-mechanical behaviour.In the third article, we have advanced our research in two key areas. Firstly, we have taken a further step in the facilitation of the Vitrimer formulation elaboration by improving the mixability of the chemical compounds. This improvement involved replacing a powder carboxylic acid with a taylor-made liquid coupling agent.Secondly, an evaluation of the thermo-mechanical behaviour of the fully cured resin was carried out, depending on the sequence of thermal and UV curing stages. The materials developed in this study have demonstrated efficacy in the effective relaxation of internal stresses, attributable to the high dynamic β-hydroxyester groups content. Consequently, processes such as reshaping, repairing, or complete recycling are enabled. Furthermore, modifications made to the resin formulations enabled the production of thermosets with customised mechanical properties. All these properties offer new possibilities for the production of parts using techniques such as 3D printing and thermal post-curing, providing a viable, sustainable and more convenient alternative for the thermosetting materials industry.
  
• MAKHARADZE, DAVIT: Design and Application of PEGylated Pseudo-Protein Nanoparticles in Drug Delivery
  
  Author: MAKHARADZE, DAVIT
  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: 15/10/2025
  Reading date: pending
  Reading time: pending
  Reading place: pending
  Thesis director: DEL VALLE MENDOZA, LUIS JAVIER
  Thesis abstract: In recent years, there has been growing interest in biodegradable and biocompatible polymeric nanoparticles (NPs) because of their versatile uses in drug delivery systems (DDSs). To improve the biological performance of these systems, PEGylation– the covalent attachment of polyethylene glycol (PEG) to the NP surface has been proposed, which improves NP stability, reduces protein adsorption, and extends circulation time.This thesis represents the synthesis, characterization, and biomedical applications of PEGylated NPs of poly(ester amide) (PEA) class. As part of the experimental work, four different types of polymer are presented in this thesis, labelled as follows: 8L6, unsaturated copolymers (precursors for PEG attachment)– [8L6]0.5-[tES-L6]0.5, (FuL6)0.5-(8L6)0.5 and their PEG conjugated adduct–PEG-PEA. Core-shell NPs are synthesized based on polymer 8L6 as a core and PEG-PEA as a shell. These biodegradable polymers (BPs) consist of the amino acid L-leucine, naturally occurring dicarboxylic acid, and diol units. The main advantages of these polymers consist of low to zero immunogenicity, high compatibility, and, at the same time, they release nutritive amino acids upon biodegradation. The polymers are designed in that way to have a non-proteinaceous molecular architecture, which is highly important to minimize immune recognition and rejection of the biomaterial.Chapter 3 describes the synthesis of polymer 8L6 and the initial design of precursor polymer [8L6]0.5-[tES-L6]0.5 for PEG attachment; the resulting PEG-PEA forms micelles similar to traditional surfactants. Additionally, stable core-shell 8L6 NPs with a size range of 100 to 200 nm were successfully prepared using the novel biodegradable surfactant PEG-PEA.Chapter 4 represents the synthesis of the new precursor (FuL6)0.5-(8L6)0.5, developed in response to the complexity and multi-step nature of the previous precursor’s synthesis. This copolymer contains equal mole percentages of unsaturated (FuL6) and saturated (8L6) repeating units designed for the special reason: the unsaturated fragments (FuL6) work for covalent attachment of PEG-derivatives and the saturated (anchoring) fragments (8L6) for immobilizing the PEGylating surfactant (PEG-PEA) to the surface of the drug-loaded NPs. Additionally, this chapter investigates the effect of various amine catalysts on the Michael addition reaction between (FuL6)0.5-(8L6)0.5 and thiol and amine-functionalized PEGs to optimize the reaction conditions.In chapter 5, the surface modification of 8L6 polymeric particles with PEG-PEA is studied by transmission electron microscopy (TEM) and synchrotron radiation-based FTIR (SR-FTIR) microspectroscopy. The formation of the core-shell structure is confirmed by both techniques.Finally, chapter 6 demonstrates the application of PEGylated 8L6 polymeric core-shell NPs. The anti-cancer potential of the phenolic compound resveratrol (RES) is evaluated using biodegradable vehicles. Those NPs are functionalized with the blood-plasma glycoprotein transferrin, which can bind to its receptor (transferrin receptor 1) highly expressed in tumour cells. The antiproliferative effects of RES-loaded NPs are studied in HeLa and U-87 cancer cell lines.Considering that synthesized NPs are biodegradable, biocompatible, and have high drug-loading capacity, this approach provides high efficiency for cancer therapy.
  

DOCTORAL DEGREE IN SIGNAL THEORY AND COMMUNICATIONS

• BOUDRIKI SEMLALI, BADR EDDINE: Analysis of Helio-Geo-Ionospheric Proxies for Earthquake Risk Prediction
  
  Author: BOUDRIKI SEMLALI, BADR EDDINE
  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: 19/09/2025
  Reading date: 12/11/2025
  Reading time: 10:30
  Reading place: Aula de Graus de l'ETSETB
  Thesis director: CAMPS CARMONA, ADRIANO JOSE | PARK, HYUK
  Thesis abstract: Earthquakes are among the most destructive natural disasters, causing significant infrastructure damage and casualties. Between 1998 and 2018, seismic events resulted in approximately 846000 fatalities and caused economic losses totaling US$661 billion, emphasizing their profound socioeconomic impact. While thousands of earthquakes occur each year globally, only a small number are significant enough to be detected by monitoring systems or felt by people. Although earthquakes cannot be prevented, efforts have been made to reduce their consequences through risk assessment and public preparedness initiatives. Despite these advances, a reliable early warning system for earthquakes remains insufficient. The absence of consistent, deterministic precursors to seismic events is a critical challenge in developing such systems. However, research has identified small detectable geophysical signal anomalies that may appear days to weeks before major earthquakes. These anomalies involve changes in thermal infrared emissions, ionospheric scintillation, disturbances in magnetic fields, etc. While these signals are not usually present, their detection could enhance forecasting capabilities. Remote Sensing (RS) is a promising technique that provides broad spatial coverage, high temporal resolution, and the capability to observe otherwise inaccessible areas, such as oceans, deserts, and mountains. RS systems allow for continuous monitoring of the Earth's surface and atmosphere, enabling the detection of potentially earthquake-related anomalies across the lithosphere, atmosphere, and ionosphere. This Ph.D. thesis studies the use of RS techniques for earthquake precursor detection and their recent advancements. It explores Lithosphere-Atmosphere-Ionosphere Coupling (LAIC), a multidisciplinary framework that explains how seismic stress and rock deformation in the lithosphere can trigger cascading effects in the atmosphere and ionosphere. The thesis also presents results from ongoing research into short- and medium-term earthquake forecasting using Earth observation data. The Ph.D. thesis examines several satellite-derived parameters: Land Surface Temperature (LST) anomalies, Ionospheric Scintillation (IS) indices, geomagnetic field variations, and space weather data. This work aims to contribute to understanding earthquake precursors and seeks to develop practical tools for better predicting seismic events, enhancing mitigation and early warning efforts.
  
• MUTHINENI, KARTHIK: Wireless Infrastructure-Based Indoor Positioning in Controlled Industrial Environments
  
  Author: MUTHINENI, KARTHIK
  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: 16/10/2025
  Reading date: 20/11/2025
  Reading time: 16:00
  Reading place: Aula Teleensenyament, Edifici B3 - Ricardo Valle Sala 103 Planta 1
  Thesis director: VIDAL MANZANO, JOSE | ARTEMENKO, ALEXANDER | NAJAR MARTON, MONTSERRAT
  Thesis abstract: Wireless communications have become the central nervous system of the Factory of the Future (FoF). According to this, wireless infrastructure in industries serves the dual purposeof providing connectivity and positioning industrial assets such as Automated Guided Vehicles (AGVs). The Non-Line-of-Sight (NLoS) and multipath-dominant environments, such asthose found in industries, hinder wireless signal propagation, leading to inaccuracies in wireless infrastructure-based positioning. While advances have been made in developing approaches to enhance wireless positioning accuracy in complex indoor scenarios, they may not be readily applicable to industrial environments without or with minimal modification. This motivates the study towards developing accurate and precise wireless infrastructure-based positioning approaches tailored explicitly for industrial settings. This PhD thesis aims to address key challenges in wireless positioning for industrial environments and develop algorithms to enhance the accuracy of wireless infrastructure-based positioning by proposing both model-driven and data-driven approaches.This PhD thesis presents simulation analysis and practical experiments to validate the efficiency of the proposed approaches using a range of different wireless infrastructures, including Fifth Generation (5G), Ultra-Wideband (UWB), and Sixth Generation (6G) mobile communication, including Integrated Sensing and Communication (ISAC). This work begins by analyzing the achievable 5G-based positioning accuracy in C-band and Millimeter-Wave (mmWave) bands within a specific model of a dense, cluttered industrial environment, utilizing high-fidelity ray tracing simulations to capture the impact of NLoS and multipath propagation. The findings highlight limitations of 5G positioning and, in general, wireless positioning in complex and cluttered industrial settings. To overcome these limitations, this work advances the State of the Art (SotA) by developing novel enhancement approaches based on sensor and/or data fusion for UWB-based and ISAC-assisted positioning systems. These approaches leverage the power of Deep Neural Networks (DNNs), Long Short-Term Memory (LSTM) networks, and Graph Neural Networks (GNNs) to model spatial and temporal relationships more effectively, yielding substantial improvements in positioning accuracy, robustness, and adaptability to dynamic industrial scenarios. Furthermore, the application of the proposed approaches is illustrated through an Automated Guided Vehicle (AGV) use case. This PhD thesis lays a strong foundation for advancing future research and real-world applications, offering valuable insights that can shape the next generation of industrial wireless positioning system design and deployment.
  
• RAFIEIAN, BARDIA: Enhancing Word and Document Embeddings for Natural Language Processing Tasks
  
  Author: RAFIEIAN, BARDIA
  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: 02/10/2025
  Reading date: 21/11/2025
  Reading time: 11:00
  Reading place: Sala d'actes de la FIB Manuel Martí Recober
  Thesis director: VÁZQUEZ ALCOCER, PERE PAU
  Thesis abstract: This thesis delves into various aspects of natural language processing, focusing on domain-specific neural machine translation, specialized word embeddings, data augmentation, recommender systems, document embedding techniques, and hierarchical classification with large language models. The work is structured around a couple of key contributions.Chapter 4 introduces a novel data preparation and tokenization method (Hybrid-BTS) for biomedical neural machine translation, alongside a post-specialization technique leveraging Wasserstein GANs to enhance word embeddings with multilingual constraints. Chapter 5 proposes a domain adaptation strategy for biomedical translation involving forward translation, BPE optimization, and term frequency manipulation. It also details the development of two fashion e-commerce recommender systems: one content-based and one collaborative, both integrating practical style rules. Finally, Chapter 6 presents an evaluation of document embedding models (Doc2vec, SciBERT, Longformer, LLaMA-3, GEMMA-2B) for long document classification, and a modular pipeline designed for multi-label hierarchical patent classification using transformer-based models, optimized for efficiency with LoRA and quantization. Collectively, these contributions push the state-of-the-art in both applied and theoretical NLP by providing new methods to boost performance, adaptability, and efficiency in domain-specific and large-scale applications.
  

DOCTORAL DEGREE IN STRUCTURAL ANALYSIS

• VENGHAUS, HENNING: Advanced Finite Element Methods for Metal Forming and Manufacturing Process Simulation: An Application to Friction Stir Welding Analysis.
  
  Author: VENGHAUS, HENNING
  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: Article-based thesis
  Deposit date: 10/09/2025
  Reading date: 10/11/2025
  Reading time: 12:00
  Reading place: Sala Zienkiewich (CIMNE) Building C1, UPC - Campus NorthGran Capitan S/N 08034 Barcelona
  Thesis director: CHIUMENTI, MICHELE | BAIGES AZNAR, JOAN | JUHRE, DANIEL
  Thesis abstract: This work explores the benefits and challenges of advanced Finite Element Methods for metal forming and manufacturing processes. As these processes become increasingly complex, FEM has emerged as a crucial tool. It helps predict physical quantities, aiding engineers in decision-making and enhancing the efficiency of development and production chains.Metal forming often involves (nearly) isochoric behavior due to plastic deformations, which can cause the standard Finite Method to become unstable. To address the isochoric behavior and ensure local convergence of strains and stresses, this study utilizes mixed finite element formulations, including the displacement-pressure (u/p), displacement-strain (u/ε), and displacement-pressure-deviatoric strain (u/p/e) formulations. To mitigate the high computational cost of the u/ε and u/p/e formulations, the Adaptive Formulation Refinement (AFR) technique is developed. This technique selectively activates the enhanced formulations based on physical criteria or error estimation. The method's accuracy and convergence rate is studied and compare favorably to reference solutions. The method is successfully applied to quasi-brittle structural failure analysis.This work further addresses the practical application of advanced numerical methods to complex manufacturing problems, notably Friction Stir Welding (FSW), which is a solid-state welding technique. FSW is characterized by isochoric deformations, extremely high strain rates, and highly non-linear and temperature-dependent material behavior. An Embedded Finite Element Method is employed to simplify the modeling of complex geometries and moving boundary conditions. It uses a purely Eulerian framework and a discrete level-set function for tool modeling and works directly with CAD tool geometries. The simulation results align well with experimental data. A parameter study of process parameters is carried out to evaluate their impact on welding forces and temperature evolution, demonstrating the tool's usefulness in aiding development processes.To enhance usability, a Graphical User Interface (GUI) is developed for creating simulation input files and managing simulations. Additionally, a particle tracing algorithm is implemented to visualize material flow. This work aims to bridge the gap between academic research and practical engineering applications. It provides advanced, yet robust and efficient numerical tools for simulating metal forming and manufacturing processes.
  

DOCTORAL DEGREE IN SUSTAINABILITY

• MEHABA, WAFA: The effect of promotions on consumer purchasing behavior
  
  Author: MEHABA, WAFA
  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: 02/10/2025
  Reading date: 18/11/2025
  Reading time: 09:00
  Reading place: Salón de Grados de la EEABB en Castelldefels
  Thesis director: GIL ROIG, JOSE MARIA
  Thesis abstract: The retail industry has long had to contend with a more competitive and complicated environment. Changes in consumer behaviour, market structures, and public health concerns have drastically altered this environment over the last 20 years. Furthermore, the importance of promotional differentiation increased as a result of economic crises, inflation, dwindling consumer purchasing power. Retail sales promotions present significant opportunities to reshape and influence the consumer behaviour. Considering their direct impact on expenditures patterns, it is crucial to understand the multifaceted promotional effects.In this context, the overall objective of this dissertation is studying the effect of sales promotions on consumer purchasing behaviour across different contexts. Three consumer studies were conducted, examining promotional effects on budget allocation, crisis driven behavioural changes and health related policy implications using Homescan data from Kantar Worldpanel. First, the effect of retail sales promotions on the allocation of the household food budget among the items of the shopping basket was investigated in Catalonia, Northeast region in Spain. Using Homescan data from purchases of a supermarket, own and cross-promotion elasticities were calculated using the Exact Affine Stone Index (EASI). Results reveal positive effects of sales promotions on households’ expenditure and mostly a negative asymmetric cross-effect, implying a small but significant budget reallocation.Second, purchasing behaviour changes during crisis periods were analysed using COVID-19 pandemic as a case study. Price sensitivity and promotional responsiveness were examined across different crisis phases and expenditure levels using both fixed effects regression and quantile regression models. Homescan data covering the period the first year of COVID-19 and the year before are used. The results indicate that households exhibit a decreased price sensitivity and reduced promotion responsiveness during the first lockdown followed by increased sensitivity during the new normality period. Additionally, during first lockdown, low expenditure households are more sensitive to prices and promotions than high expenditure households. Third, a cross-country comparative analysis is conducted. The relationship between retail sales promotions and Body Mass Index (BMI) is examined using the EASI demand system, comparing northern (Scotland) and southern (Spain) regions. The analysis focuses on foods High in fat, Sugar and Sodium (HFSS) across different BMI profiles. Findings indicate that consumers with unhealthy BMI exhibit higher sensitivity to price and expenditure changes compared to those with healthy BMIs. Moreover, Scottish households show greater sensitivity to expenditure changes and promotions compared to their Spanish counterparts.The research conducted in this dissertation provides valuable insights to retailers, policymakers, and other stakeholders involved in the food retail sector. The outcomes of this dissertation can guide promotional strategy design, pricing decisions, and policy interventions to meet consumer needs while addressing broader societal concerns including crisis management and public health objectives.
  
• URIOSTE DAZA, SERGIO ALEJANDRO: Advancing Reform of European Union Plant Variety Registration: Institutional, Empirical, and Policy Insights for Sustainable Agri-Food Governance
  
  Author: URIOSTE DAZA, SERGIO 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 SUSTAINABILITY
  Department: University Research Institute for Sustainability Science and Technology (IS.UPC)
  Mode: Normal
  Deposit date: 29/09/2025
  Reading date: 12/12/2025
  Reading time: 12:00
  Reading place: Sala de Grados de la ESAB
  Thesis director: TAGHOUTI, IBTISSEM | GIL ROIG, JOSE MARIA
  Thesis abstract: Plant variety registration is a critical regulatory gatekeeper between the breeding of improved varieties and their farm-level adoption. In the European Union, however, this system is being outpaced by technological advances and sustainability challenges. Legislative reform is now underway to address these shortcomings, aiming to improve the system’s efficiency by integrating new technologies and sustainability criteria into variety testing and fostering greater harmonisation across Member States. Although these reforms are broadly welcomed, diverging positions among stakeholder groups and EU institutions remain unresolved.Bridging these differences will require robust evidence to inform the ongoing negotiations. This thesis responds to this demand by providing an evidence-based assessment of the current system’s inefficiencies and by proposing realistic reform pathways to help reconcile core tensions between regulatory drag and productivity, divergent stakeholder interests, and the gap between policy goals and farm-level realities. To achieve these objectives, this research presents an integrated framework that engages all key actors in variety testing and combines econometric analysis, decision-analytic modelling, and qualitative analysis.Using a large panel dataset on crop registration and productivity, a fixed-effects analysis provides evidence of regulatory drag on productivity gains, particularly for crops subject to Value for Cultivation and Use testing. Evidence gathered from stakeholders explores into the factors behind these regulatory delays and identifies pathways to overcome systemic inefficiencies, including the uptake of enabling technologies and the harmonisation of testing processes.Subsequently, an analysis of contested policy alternatives is conducted using a replicable framework that integrates expert judgment with public input through multi-criteria decision methods and complementary weighting techniques. The results reveal a clear consensus on prioritising the adoption of technological advancements to improve the system's efficiency and accuracy. However, the analysis also exposes disagreements over efforts to harmonise the system and include sustainability criteria in testing procedures, revealing significant heterogeneity among stakeholder groups.To further investigate the contested issue of adding sustainability criteria in variety testing, a farm-level study is presented to elicit the preferences of apple growers in Spain. Using a Discrete Choice Experiment, farmers' preferences for sustainability traits in new apple varieties were elicited and examined in relation to risk behaviours. The results show a positive but heterogeneous demand for sustainability-related traits, with willingness-to-pay shaped by farm characteristics rather than by measured risk attitudes.Together, these findings demonstrate how data-driven and stakeholder-informed reforms can reduce institutional friction by establishing common ground for negotiation on key aspects of the regulation. Both stakeholder priorities and farmers’ demands point toward the need to prioritise technological uptake and design mechanisms that facilitate the delivery of climate-resilient and resource-efficient varieties. Effective stakeholder involvement and continuous evidence generation are essential for the regulatory path forward. By integrating evidence across institutional, technological, and behavioural layers, this research advances the goals of the European Green Deal and the Farm to Fork Strategy and provides a transferable framework for future assessments of agricultural policy and innovation.
  

DOCTORAL DEGREE IN THEORY AND HISTORY OF ARCHITECTURE

• PEDRAGOSA BATLLORI, GEMMA: Santa Coloma d'Andorra: el projecte d'una església a l'Andorra d'abans del S.XI.
  
  Author: PEDRAGOSA BATLLORI, GEMMA
  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 THEORY AND HISTORY OF ARCHITECTURE
  Department: Department of History and Theory of Architecture and Communication Techniques (THATC)
  Mode: Normal
  Deposit date: 01/09/2025
  Reading date: 21/11/2025
  Reading time: 10:00
  Reading place: ETSAB (Escola Tècnica Superior d'Arquitectura de Barcelona) - Planta Soterrani - Sala d'Actes Av. Diagonal, 649-651 - 08028 - Barcelona
  Thesis director: GRANELL TRIAS, ENRIQUE | GINER OLCINA, JOSEP
  Thesis abstract: The church of Santa Coloma d’Andorra belongs to one of the simplest and oldest architectural types of religious architecture: that of a single rectangular nave with a square apse. However, the simplicity of this type should not necessarily be associated with a straightforward or immediate construction or design.The aim of this study is to determine the extent to which the architecture of Santa Coloma follows a complex metrical design, which could only be achieved within a cultural context that, in Santa Coloma — located near two major cultural centres of the time, the Cathedral of La Seu d’Urgell and the Monastery of Sant Serni de Tavèrnoles — is highly plausible.In this work, architecture is used as archaeological material to analyse the key elements of the building’s architectural composition. Historiography has been reviewed, plans have been drawn up, the unit of measurement has been identified, and its dimensions studied in relation to the knowledge of proportion of the period and descriptions of biblical buildings. And it turns out that in order to conceive, design and build an apparently simple church like this, it was necessary to be familiar with the architecture represented in the Bible and with the arithmetical elaborations compiled by Boethius and Cassiodorus in the 6th century.We will therefore see a building which, although rural and seemingly modest, is the result of a layout and proportions based on a specific symbolic language, reflecting and documenting a body of knowledge and a way of applying it.
  

DOCTORAL DEGREE IN URBAN AND ARCHITECTURAL MANAGEMENT AND VALUATION

• CARRILLO CERVERA, ALEJANDRO: Efecto del área verde en la salud: parques y enfermedades cardiovasculares en Mérida (2020)
  
  Author: CARRILLO CERVERA, 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 URBAN AND ARCHITECTURAL MANAGEMENT AND VALUATION
  Department: Department of Architectural Technology (TA)
  Mode: Normal
  Deposit date: 25/09/2025
  Reading date: pending
  Reading time: pending
  Reading place: pending
  Thesis director: ROCA CLADERA, JOSE NICASIO | ARELLANO RAMOS, BLANCA ESMARAGDA
  Thesis abstract: This doctoral study analyzes the relationship between urban green spaces and cardiovascular disease (CVD) mortality in the city of Mérida, Yucatán (Mexico), using a pioneering methodology at the urban block scale. The research combined spatial analysis, statistical techniques, and direct observation to assess the impact of green coverage, quality (NDVI), and accessibility to urban parks on public health.In Mérida, the analysis reveals that daily proximity to green areas—especially small or medium-sized parks within 300 meters—has a statistically significant effect (β = –0.001; p < 0.001). These parks exhibit the highest quality of urban greenery and the best plant health. In contrast, larger parks over 5 hectares, where vegetation quality and plant health are poor, show no significant effect (β = 0.000; p = 0.006). This suggests that it is not only the amount of permeable land in parks that matters, but also the quality of urban vegetation and accessibility in the immediate surroundings. By age group, young adults (18–24 years) had better access to parks, while children and older adults faced more barriers.People with disabilities are more vulnerable to developing CVD (β = 0.324; p = 0.010). The findings also indicate an increase in mortality associated with the use of private vehicles as a means of transportation (β = 0.261; p = 0.007). On the other hand, higher spending on food consumed at home has a protective effect (β = –0.165; p < 0.001).Although other factors may also explain changes in mortality rates, urban greenery and the quality of the built environment significantly influence population health. The regression equation was statistically significant F(5, 10036) = 0.001. The R² value was 0.202, indicating that 20.2% of the CVD mortality rate can be explained by the model, which includes variables such as accessibility to urban parks, the percentage of people with disabilities, spending on private vehicle use, and spending on food consumed at home.The mortality rate decreases by –0.001 points for every additional square meter of urban greenery in parks. Excluding medians and sidewalks, the population has access to an average of 7.17 m² of urban green space per inhabitant across all parks within the peripheral ring, and 5.79 m² per inhabitant when considering only parks of 5 hectares or more. The average CVD mortality rate is 29.78% per urban block.
  

DOCTORAL DEGREE IN CHEMICAL PROCESS ENGINEERING

• ESPEJO DELGADO, VICENÇ: Analysis and modelling of explosions in gas-fired combustion chambers
  
  Author: ESPEJO DELGADO, VICENÇ
  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 CHEMICAL PROCESS ENGINEERING
  Department: Department of Chemical Engineering (EQ)
  Mode: Normal
  Deposit date: 28/10/2025
  Deposit END date: 10/11/2025
  Thesis director: CASAL FABREGA, JOAQUIM | PLANAS CUCHI, EULALIA
  Thesis abstract: Combustion chambers are a common equipment widely used in many industries to retrieve heat from fuels (such as in boilers, furnaces, and other fired heaters). Despite the well-documented explosion hazards associated with this equipment, accidents continue to be reported periodically in the industry. The consequences of such events can be catastrophic, leading to severe damages to the equipment, surrounding structures or other equipment, as well as injuries or fatalities.This thesis focusses on the study combustion chamber gas-fired explosion scenarios as a result of the accumulation of unburned gas inside the firebox until flammable conditions are reached, and ignition occurs. As an initial step, a historical analysis of accidents was conducted to typical accident sequences and to highlight the importance of different contributing causes. The main objective of the work is therefore to study these scenarios and provide insights that may improve safety protection design, risk assessments and engineering practices for gas-fired combustion chambers in industrial applications.Some experimental work was found during bibliographic research for similar geometry enclosures, but limited in size, up to 64 m3. However, industrial combustion chambers can reach volumes of thousands of cubic meters. Full-scale experimentation on such equipment would be costly and would require extensive infrastructure to contain, isolate and monitor the explosions. As an alternative, this research employs simulations with FLACS, a Computational Fluid Dynamics (CFD) software widely validated for explosion scenarios, to study the considered explosion cases.The effects of explosions and their dependence on combustion chamber geometry characteristics were evaluated taking special attention on the influence of explosion panels, internal elements, chamber size, as well as the fuel used in explosion development and maximum peak pressures reached. To assess the external effects on the surroundings, an adaptation of the TNT-equivalency model was developed, providing a method to adjust the model yield factor to the combustion chamber explosion consequences.Finally, fuel dispersion inside the chamber was simulated to characterize how unburned fuel evolves over time when introduced through burners. These scenarios were related with the explosion sequences identified in the historical accident analysis. The influence of furnace duty, fuel premixing with air, and burner configuration (single vs. multiple burners) was investigated. Ignition of the accumulated fuel at different dispersion times resulted in explosions with different fuel amounts and concentrations and was also assessed to capture the impact of explosion onset at different stages of scenario evolution. The outcomes of this thesis highlight the effect of key combustion chambers characteristics on explosion phenomena. The evolution of the scenarios identified a critical “trend shift” period, that corresponded to the the timeframe to reach hazardous concentrations. Sensitivity analyses considering different fuels, air pre-mixing, burner configurations or maximum duty per chamber volume revealed general trends applicable to other combustion chamber designs. Overall, the findings provide valuable insights into explosion phenomena in combustion chambers, and offer practical guidance for safer systems design, as well as safeguards effectiveness criteria to be considered in risk assessments.
  

DOCTORAL DEGREE IN CIVIL ENGINEERING

• TARIN TOMAS, JUAN CARLOS: Optimización de dispositivos flexoeléctricos.
  
  Author: TARIN TOMAS, JUAN CARLOS
  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: 29/10/2025
  Deposit END date: 11/11/2025
  Thesis director: ARIAS VICENTE, IRENE | GRECO, FRANCESCO
  Thesis abstract: This thesis develops a strategy to study the optimization on flexoelectric devices. There are nowadays many electromechanical devices , sensors, actuators and energy harvesters, that rely on the basis of the well-known piezoelectric effect, but not all materials exhibit this effect. The most widely used piezoelectric materials show limitations in terms of fracture toughness, toxicity, biocompatibility and temperature range of operation. A novel alternative is provided by flexoelectricity, which, unlike piezoelectricity, appears in all dielectric materials. Flexoelectricity is a size dependent electromechanical coupling which manifest itself at submicron scales and relies on the generation of field gradients inside the material. It has been recently shown, that the flexoelectric response to field gradients in the materials can be conveniently accumulated to produce a macroscopic effective piezoelectric-like response by material architecture. Through the suitable geometry of a repeating unit, piezoelectric metamaterials can be conceived to produce a net electromechanical response even when built from non-piezoelectric base materials, and thus devoid of some of the above mentioned limitations. The design of such piezoelectric metamaterials exploiting flexoelectricity poses numerous challenges both theoretical and computational. Flexoelectricity is a gradient-mediated property, and thus requires additional physical and engineering intuition beyond the homogeneous setups of piezoelectricity. The governing equations of flexoelectricity are a coupled system of fourth-order PDEs, which require solution methods beyond standard finite elements providing the required continuity. In recent work, these issues have been addressed in detail, identifying the main design concepts for piezoelectric metamaterials and developing suitable solution methods. In the present thesis, we focus on the systematic rational design of piezoelectric metamaterials and devices exploiting the flexoelectric effect. A useful tool towards this goal is topology and shape optimization with multiple and possibly conflicting objectives. An important challenge is the high-computational cost of solving flexoelectric boundary value problems in general geometries. We will thus aim at devising efficient optimization strategies to reduce the computational cost, introducing machine learning techniques to alleviate the need for detailed and accurate simulations for every design in the optimization process.
  

DOCTORAL DEGREE IN COMPUTER ARCHITECTURE

• ALLKA, XHENSILDA: Enhancing Data Quality in IoT Monitoring Sensor Networks
  
  Author: ALLKA, XHENSILDA
  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: 31/10/2025
  Deposit END date: 13/11/2025
  Thesis director: BARCELÓ ORDINAS, JOSE MARIA | GARCÍA VIDAL, JORGE
  Thesis abstract: In recent years, technological development and an increased number of cars among other factors, have influenced air pollution levels. This increase in levels has also increased the need to monitor them, as they are directly related to human health and the economy. To monitor air pollution, the government has deployed precise monitoring stations, which are expensive to deploy and maintain. Due to their cost, they are not widely distributed. However, since air pollution can change over short distances, the distribution of these stations can be insufficient. Recently, a solution has emerged: the use of low-cost sensors (LCSs), which provide broader coverage at a much lower cost. However, these LCSs have one drawback: the quality of the data they provide is poor.Current research in this field has employed machine learning (ML) models to calibrate these LCSs, thereby enhancing the quality of the data they provide. In an Internet of Things (IoT) monitoring network, the quality of data is closely associated with decision-making processes. This thesis focuses on enhancing the data quality provided by the LCSs from two perspectives: improving calibration performance and detecting anomalies and outliers. The objective of both of these perspectives is to ensure data accuracy and reliability.The first part of the thesis focuses on the improvement of the calibrated data provided by the LCSs and the detection of the concept drift and the update of the parameters of the current calibration model such that it adapts to the new conditions. We are enhancing the quality of the calibrated data by implementing a model pattern-based approach. Our proposed methods, Temporal Pattern Based Denoising (TPB-D) and Temporal Pattern Based Calibration (TPB-C), improve the quality of the calibrated data. Given that environmental conditions are subject to change over time, it is essential to update the parameters of the calibration model. We proposed Window-based Uncertainty Drift Detection and Recalibration (W-UDDR), a system capable of detecting the presence of concept drift (i.e., environmental changes).The second part of the thesis focuses on the reliability of the data. Sensors, regardless of their cost, are often prone to irregularities such as outliers, anomalies, or drift, which can be caused by various factors. It is critical to identify these irregularities, as the data will be incorporated into the training of the model related to other tasks. In this thesis, three distinct scenarios were examined. The first one is related to the detection of outliers in the edge. In this case, we proposed the Edge Streaming Outlier Detection (ESOD) framework. ESOD is a simple and lightweight framework that can identify outliers in the edge with a limited amount of memory. The system offers two approaches: real-time and near real-time. The near real-time approach involves minor delays in decision-making. The second approach is related to the detection of more complex irregularities, such as anomalies in a given sensor. This scenario is distinct from the first one in that it offers offline anomaly detection capabilities. We proposed spatiotemporal correlation recurrent autoencoder anomaly detection (STC-RAAD), which demonstrated satisfactory performance in detecting anomalies in a given sensor. It is worth noting that the third scenario pertains to the detection and localization of anomalies in a network of sensors. This is of particular relevance in scenarios where the identification and precise location of the source of an anomaly are crucial. We hereby propose a pattern-based attention recurrent autoencoder anomaly detection (PARAAD) method. This method is designed to detect and localize anomalies in sensors.
  
• BARRERA HERRERA, JAVIER ENRIQUE: Improving Time Predictability and Code Coverage of Embedded GPUs for Real-Time Systems
  
  Author: BARRERA HERRERA, JAVIER ENRIQUE
  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: 07/11/2025
  Deposit END date: 20/11/2025
  Thesis director: CAZORLA ALMEIDA, FRANCISCO JAVIER | KOSMIDIS, LEONIDAS
  Thesis abstract: This dissertation addresses challenges that the adoption of GPUs in Critical Embedded Systems (CES) faces, namely, Time Predictability and Code Coverage. Different domains that deploy CES are constantly adding Artificial Intelligence (AI)-based features, such as autonomous driving, that demand high performance levels. Multi-Processors Sytem-on-Chip (MPSoCs) are widely used to provide said performance levels, as they are equipped with accelerators, among which, Graphics Processing Units (GPUs) are a common choice. However, CES must undergo a rigorous Verification and Validation (V&V) process, in which a certain level of Execution Time Determinism (ETD) must be guaranteed. The use of several tasks to increase the overall utilization introduces contention in shared resources, which induces time variability. To provide the ETD guarantees, the time variability must be either mitigated or tracked and controlled. Another challenge for the adoption of GPUs in CES, is that the V&V process demands evidence of the thoroughness of the testing phase, for which Code Coverage is used as a test quality indicator. However, Code Coverage, as traditionally understood for sequential CES does not cover all possible scenarios in which a GPU thread might execute.For low-criticality and mixed-criticality CES, we contend that we can allow tasks to share the Last Level Cache (LLC) if hardware support for contention tracking is provided. Providing a clear understanding on how tasks contend with each other enables CES developers to balance performance and time predictability. For high-criticality CES, it is a common practice to implement LLC partitioning as it allows tasks to access LLC without suffering from inter-kernel contention, however, tasks may experience a performance loss due to lack of resources. In this Thesis, we propose Demotion Counters, a novel technique that tightly tracks how much each task has been demoted towards eviction in the LLC, thus, effectively quantifying their impact in CES. Additionally, we also assess the use of NVIDIA’s Multi-Instance GPU (MIG) feature as means to improve ETD in high-criticality CES.Code Coverage is used as a test quality indicator to provide evidence of the thoroughness of the testing, as required by the V&V process. However, if applied as traditionally understood, it will ignore the threading dimension of GPUs. Threads have private regions of memory, as well as shared regions at different granularities. This means that errors that are innocuous to one thread are potentially harmful for another, hence, it does not cover all possible cases under which GPU threads might execute. In this Thesis, we propose the use of Per-Thread Statement Coverage (PTSC), which tracks the Code Coverage at thread granularity. In order to mitigate the overheads caused by PTSC, several variants that apply different orthogonal optimizations are also proposed. Finally, we also evaluate the potential benefits of using hardware support for PTSC, mitigating the memory consumption of PTSC, as well as the execution time impact at deployment.Summarizing, this Thesis advances the state of the art in the adoption of GPUs in CES. The proposal of hardware contention tracking support and assessment of NVIDIA’s MIG, as means to improve ETD, effectively tackles the Time Predictability challenge in shared LLC. The proposal of software PTSC allows providing CES designers with the whole picture of the execution in commercially available GPUs. The use of hardware support for PTSC mitigates the overheads of software PTSC in deployment, while the different compression techniques reduce the volume of information during testing phase without losing data. Therefore, this Thesis provides means to face the Time Predictability and Code Coverage challenges of GPUs in CES.
  
• KHABBAZAN, BAHAREH: Improving Memory-centric Architectures for Accelerating Cognitive Computing Workloads
  
  Author: KHABBAZAN, BAHAREH
  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: 31/10/2025
  Deposit END date: 13/11/2025
  Thesis director: RIERA VILLANUEVA, MARC | GONZÁLEZ COLÁS, ANTONIO MARIA
  Thesis abstract: The rapid advancements in deep neural networks (DNNs) have led to increasingly complex and memory-intensive workloads, posing significant challenges for traditional computing architectures. Excessive data movement, computational inefficiencies, and energy constraints limit the scalability of DNN accelerators. This thesis addresses these challenges by proposing memory-centric approaches to optimize DNN execution through efficient quantization, in-memory processing, and data movement reduction.We first introduce DNA-TEQ, an adaptive exponential quantization scheme that minimizes memory footprint and eliminates the need for conventional multipliers, significantly enhancing energy efficiency. Experimental results show that DNA-TEQ reduces the memory footprint by 40% on average compared to the 8-bit integer baseline. The hardware processing-near-memory (PnM) accelerator designed to benefit from DNA-TEQ further improves inference latency by 1.5× while maintaining accuracy comparable to full-precision models.Next, we present QeiHaN, a PnM accelerator that employs base-2 exponential quantization and an implicit bit-shifting technique to reduce redundant memory accesses and optimize DNN inference. Our evaluations demonstrate that QeiHaN reduces memory movement by 67%, leading to a 4.2× speedup in execution time and a 3.5× reduction in energy consumption compared to conventional baseline architectures.Lastly, we propose Lama, a lightweight memory access mechanism that enhances lookup table (LUT)-based processing-in-memory (PuM) architectures by enabling parallel, column-independent accesses within DRAM mats, supporting up to 8-bit integer SIMD operations for large-scale models. The experimental results show that Lama significantly reduces memory commands for SIMD operations compared to the state-of-the-art PuM techniques. We further leverage Lama to design LamaAccel, an HBM-based large language model (LLM) accelerator that executes efficiently without modifying DRAM timing parameters. LamaAccel outperforms GPUs by up to 19×, achieving substantial energy savings in low-precision layers.The proposed techniques collectively reduce data movement, optimize memory utilization, and improve computational efficiency. Our findings demonstrate that memory-centric approaches can significantly enhance DNN acceleration, offering scalable and energy-efficient solutions for next-generation AI systems.
  
• SABRI ABREBEKOH, MOHAMMAD: Improving Efficiency of ReRAM-Based Accelerators for Cognitive Computing Workloads
  
  Author: SABRI ABREBEKOH, 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: 07/11/2025
  Deposit END date: 20/11/2025
  Thesis director: GONZÁLEZ COLÁS, ANTONIO MARIA | RIERA VILLANUEVA, MARC
  Thesis abstract: Deep Neural Networks (DNNs) have achieved remarkable success across a wide range of applications. The main operation in DNNs is the dot product between quantized input activations and weights. Previous works have proposed memory-centric architectures based on the Processing-in-Memory (PuM) paradigm. ReRAM technology is especially appealing for PuM-based DNN accelerators because of its high density for weight storage, low leakage energy, low read latency, and high-performance capabilities to perform DNN dot products massively in parallel within ReRAM crossbars. However, there are three main bottlenecks in ReRAM-based accelerators.First, the energy-hungry Analog-to-Digital Converter (ADC) required for in-ReRAM analog computations, which undermines the efficiency and performance benefits of PuM. To improve energy efficiency, we present ReDy, a hardware accelerator that implements a novel ReRAM-centric dynamic quantization scheme, leveraging bit-serial streaming and processing of activations. The energy consumption of ReRAM-based DNN accelerators is directly proportional to the numerical precision of input activations in each layer. ReDy exploits the fact that activations in convolutional layers are often grouped according to filter sizes and crossbar dimensions. It quantizes each group of activations on-the-fly with different precision levels, based on a heuristic that considers the statistical distribution of each group. Overall, ReDy significantly reduces ReRAM crossbar activity and the number of A/D conversions compared to static 8-bit uniform quantization. Evaluated on a set of modern CNNs, ReDy achieves on average 13% energy savings over an ISAAC-like accelerator, with negligible area overhead.Second, the costly writing process of ReRAM cells has led to accelerators designed with enough crossbar capacity to store entire DNN models. Given the continuous growth of DNN model sizes, this approach is infeasible for some networks and inefficient due to huge hardware requirements. These accelerators lack flexibility and face an adaptability challenge. To address this, we introduce ARAS, a cost-effective ReRAM-based accelerator that uses a smart scheduler to adapt various DNNs to resource-limited hardware. ARAS also overlaps computation of one layer with weight writing of others to mitigate high ReRAM write latency. Furthermore, ARAS introduces optimizations to reduce the energy overhead of ReRAM writes, including re-encoding weights to increase similarity across layers and reduce energy when overwriting cells. Overall, ARAS significantly reduces ReRAM write activity. Evaluated on multiple DNN models, ARAS delivers up to 2.2× speedup and 45% energy savings compared to a baseline PuM accelerator without optimizations, and up to 1.5× speedup and 62% energy savings compared to a TPU-like accelerator.Third, ReRAM cells suffer from limited endurance due to wear-out caused by repeated updates during inference, reducing the lifespan of ReRAM-based accelerators. Overcoming this endurance limitation is essential for making such accelerators viable in long-term, high-performance DNN inference. To address this, we propose Hamun, an approximate computing method designed to extend the lifespan of ReRAM-based accelerators through multiple optimizations. Hamun introduces a mechanism to detect and retire faulty cells caused by wear-out, preventing them from degrading accuracy. It also applies wear-leveling techniques across different abstraction levels and introduces a batch execution scheme to maximize cell utilization across inferences. Additionally, Hamun leverages the fault-tolerance of DNNs with a new approximation method that delays cell retirement, reducing the performance penalty and further extending lifespan. Evaluated on a set of DNNs, Hamun improves lifespan by 13.2× over a state-of-the-art baseline, with its main contributions coming from fault handling (4.6×) and batch execution (2.6×).
  

DOCTORAL DEGREE IN EARTHQUAKE ENGINEERING AND STRUCTURAL DYNAMICS

• WANG, FUMIN: A Simplified Approach for the Seismic Analysis of Compliant Soil Slopes
  
  Author: WANG, FUMIN
  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 EARTHQUAKE ENGINEERING AND STRUCTURAL DYNAMICS
  Department: Department of Civil and Environmental Engineering (DECA)
  Mode: Normal
  Deposit date: 07/11/2025
  Deposit END date: 20/11/2025
  Thesis director:
  Thesis abstract: Over the past few decades, post-earthquake investigations have revealed extensive damage to both natural and engineered slopes, including landslides, slope collapses, and other forms of instability. These observations highlight the urgent need to improve our understanding of slope behavior under seismic loading and to refine analytical approaches for stability assessment. This thesis develops simplified yet robust methods for evaluating the seismic stability and dynamic performance of slopes, balancing physical insight with analytical efficiency.Chapter 2 reviews existing analytical frameworks —including the traditional Newmark rigid block, decoupled, and coupled analyses— used to estimate slope stability and dynamic responses under earthquake excitation. It compares their performance, identifies limitations, and outlines research needs.Chapter 3 proposes an extremely simplified and computationally efficient formulation for estimating the Factor of Safety (FS) of co-seismic compliant slopes, explicitly accounting for soil slope, ground motion direction, and groundwater effects. The method clarifies the differences between coupled and Newmark-type analyses and introduces a modified yield acceleration for compliant slopes. Validation through experimental, numerical, and field data confirms its simplicity and accuracy.Chapter 4 presents, as well, a coupled linear analysis based on a simplified two-block flexible model subjected to harmonic seismic excitation. Analytical and numerical solutions are derived and verified, and sensitivity analyses reveal the influence of ground motion direction, amplitude of input motion, damping ratio, and slope properties. Simplified upper bounds for maximum acceleration and displacement for the first cycle of rigid and compliant slopes are proposed, demonstrating the model’s predictive capability despite its simplicity.Chapter 5 examines the performance of classical discrete lumped-mass models against continuous wave-equation-based formulations under sinusoidal seismic loading without sliding. Both approaches yield consistent results for relative displacement and shear stress. It also shows that both models predict similar failure patterns, with cohesion playing a key role in shifting the critical sliding surface from the surface to deep depth. Incorporating Maxwell-type damping into the continuous model proves valid as an alternative to the traditional Kelvin-Voigt model in linear analysis.As a summary, this thesis extends and generalizes the simplified traditional methods for seismic analysis of soil slopes, including the stiffness of the sliding material. The proposed methodologies, validated through published experiments and real cases, provide an efficient and reliable framework for seismic slope stability assessment and offer valuable guidance for future research and engineering practice.
  

DOCTORAL DEGREE IN NETWORK ENGINEERING

• BAZÁN GUILLÉN, ALBERTO: Contribution to smart charging for electric vehicles in urban environments
  
  Author: BAZÁN GUILLÉN, 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 NETWORK ENGINEERING
  Department: Department of Network Engineering (ENTEL)
  Mode: Normal
  Deposit date: 28/10/2025
  Deposit END date: 10/11/2025
  Thesis director: AGUILAR IGARTUA, MONICA | BARBECHO BAUTISTA, PABLO ANDRES
  Thesis abstract: The transition toward sustainable urban mobility and the urgent need to reduce greenhouse gas emissions have positioned electric vehicles (EVs) as a key element in the transformation toward a cleaner, more efficient, and smarter transportation system. The progressive increase in the number of EVs makes it essential to develop a robust and flexible charging infrastructure capable of meeting the growing energy demand while minimizing the impact on the power grid. In this context, coordinated charging planning becomes essential to optimize both user comfort and the efficiency of the electric and urban systems, particularly in dense environments and Mobility Hubs.This thesis addresses two fundamental and complementary challenges within the field of sustainable urban mobility: realistic traffic generation and optimal EV charging scheduling. To support decision-making in urban planning, DesRUTGe (Decentralized Realistic Urban Traffic Generator) has been developed — a new simulation framework that integrates Deep Reinforcement Learning (DRL) techniques with the SUMO simulator to generate high-fidelity, time-varying traffic profiles over 24 hours. Its main contribution lies in the incorporation of Decentralized Federated Learning (DFL), where each traffic detector and its associated area act as autonomous nodes that train local models with minimal historical data and exchange knowledge with nearby nodes. This strategy produces more accurate and realistic traffic patterns than those generated by centralized methods or conventional tools such as RouteSampler, achieving a better representation of daily variations and congestion peaks.Building upon this realistic simulation environment, the thesis proposes an intelligent EV charging scheduling framework designed for urban Mobility Hubs. The scheduler considers key factors such as time-varying electricity prices, vehicle charging time windows, initial and target State of Charge (SoC), and the possibility of bidirectional operation (vehicle-to-grid) when applicable. The work analyzes and compares two main approaches: Mixed-Integer Linear Programming (MILP), which provides optimal solutions in small-scale scenarios but has limited scalability, and Reinforcement Learning (RL) methods based on Proximal Policy Optimization (PPO), which demonstrate robust, adaptive, and efficient performance in more complex and dynamic environments. Additionally, reverse charging strategies are explored, allowing EVs to return energy to the grid during peak demand periods, thereby generating economic incentives for drivers and improving grid stability.Overall, this research makes three main contributions: (i) It introduces a decentralized and highly realistic simulation platform for generating urban traffic profiles; (ii) it develops a scalable and adaptive framework for optimal electric vehicle charging management, balancing user comfort, grid stability, and environmental objectives; and (iii) it demonstrates how both methodologies can be integrated into smart city planning, enabling coordinated design of sustainable mobility services and more efficient energy management. The results, validated with real-world data from the city of Barcelona, demonstrate the feasibility and potential impact of the proposed solutions for developing more sustainable and resilient urban mobility systems.
  
• TORRES PÉREZ, CLAUDIA: Energy-Aware Service Placement Strategies in Dynamic Edge Environments
  
  Author: TORRES PÉREZ, CLAUDIA
  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 NETWORK ENGINEERING
  Department: Department of Network Engineering (ENTEL)
  Mode: Normal
  Deposit date: 28/10/2025
  Deposit END date: 10/11/2025
  Thesis director: CERVELLO PASTOR, CRISTINA | CORONADO CALERO, ESTEFANÍA | SIDDIQUI, MUHAMMAD SHUAIB
  Thesis abstract: The evolution of Beyond 5G (B5G) networks is transforming mobile communications, enabling interconnected environments and the proliferation of latency-sensitive services, such as real-time Internet of Things (IoT) analytics, immersive Extended Reality (XR) experiences, and generative Artificial Intelligence (AI) as a service. At the forefront of this transformation, edge computing is emerging as a pivotal paradigm extending computation resources closer to end users. Within this spectrum, Multi-Access Edge Computing (MEC) plays a central role by providing standardized, cloud-like capabilities at the edge of access networks. However, the inherent dynamism and distributed nature of MEC, especially in extreme-edge environments, adds significant complexity. The dynamism stems from variable service requirements such as fluctuating workloads, coupled with the dynamic infrastructure that includes heterogeneous nodes, variable connectivity, bandwidth requirements, and mobility across the entire Edge-to-Cloud continuum. Consequently, service placement in these dynamic settings faces substantial challenges, demanding adaptive and context-aware strategies to achieve system efficiency. Within this landscape, the growing energy demands of distributed edge nodes emerge as a paramount concern, contributing to significant carbon emissions, undermining global sustainability efforts, but also driving up operational costs, potentially rendering large-scale edge deployments financially unsustainable. Additionally, many edge devices rely on limited power sources, making energy efficiency essential for extending their operational lifetime and ensuring system reliability. As MEC scales geographically, the cumulative energy cost becomes a critical bottleneck for widespread adoption and sustainable growth. Under these volatile and resource-intensive conditions, minimizing energy consumption and optimizing resource utilization becomes critical. Thus, effective placement strategies must address dynamic constraints and ensure long-term sustainability. This thesis proposes intelligent, adaptive, and energy-efficient service placement mechanisms in distributed and extreme-edge environments. Firstly, it introduces an AI-based novel distributed orchestration technique within Distributed Multi-MEC Systems (DMMS). The technique, named Distributed Deep Reinforcement Learning-based Service Placement Availability-Aware Algorithm (DDRL-SP3A), aims to efficiently implement services in a system coordinated by multiple orchestrators, thereby optimizing resource usage by minimizing the number of active nodes. Secondly, the thesis presents an AI-based energy-aware strategy for heterogeneous MEC infrastructures introduced as DDRL-based Energy-Aware Service Placement Algorithm (DDRL-EASPA). The aim is to reduce the number of active nodes under heterogeneous infrastructures and dynamic service demands. Thirdly, this effort introduces an Energy Minimization Service Placement Algorithm (EMSPA), an adaptive, heuristic-based placement method to minimize energy consumption in smart factory extreme-edge environments, characterized by high mobility and severe resource and connectivity constraints. The proposed solutions achieve a near-optimal, efficient performance in low-latency MEC scenarios while meeting service and infrastructure constraints, optimizing resource utilization, and minimizing energy consumption. These solutions are evaluated through simulations of distributed networks with numerous hosting devices, orchestration entities, and service workloads. Additionally, a series of evaluations are conducted in a real-world testbed, demonstrating the differences in service placement performance compared to simulation. Overall, the strategies proposed in this thesis provide a robust and applicable framework for sustainable and high-performing edge computing.
  

DOCTORAL DEGREE IN NUCLEAR AND IONISING RADIATION ENGINEERING

• AL AWAD, ABDULRAHMAN: Multiscale Multiphysics Investigation of Helium Bubble Formation and Dynamics in Liquid Lead-Lithium Eutectic
  
  Author: AL AWAD, ABDULRAHMAN
  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 NUCLEAR AND IONISING RADIATION ENGINEERING
  Department: Department of Physics (FIS)
  Mode: Normal
  Deposit date: 03/11/2025
  Deposit END date: 14/11/2025
  Thesis director: BATET MIRACLE, LLUIS | SEDANO MIGUEL, LUIS ANGEL
  Thesis abstract: Liquid metals (LMs) and their alloys are characterized with excellent thermophysical and dynamic properties for heat transport purposes, thus rendering them as promising candidates in advanced energy-production technologies such as the nuclear fusion energy. Liquid lead-lithium eutectic (LLE) alloy remains a key medium of the various breeding blanket (BB) concepts of the EU DEMO design. In LLE-BBs, helium (He) is produced in a mole-to-mole ratio with tritium by nuclear reactions, and technical concerns regarding the state of He in liquid LLE alloy have been raised since the 1990s. Gas-bubble nucleation in weak liquid–gas solutions has been a challenging topic in theory, experimentation, and computer simulations, especially given the expected very low solubility of He in LMs and the scarcity of experimental data. Despite the continuous efforts, the He nucleation issue still lacks conclusive findings and robust estimations of relevant parameters, and the main objective of this thesis is to exploit ab initio (AIMD) and classical molecular dynamics (CMD) simulations in order to shed light on the underlying physics and theory, and to estimate the thermodynamic and kinetic conditions required for He bubbles to nucleate and grow in a manner that facilitates the integration of results and findings into macroscopic models, e.g., CFD models, for engineering design and nuclear safety purposes. Additionally, it aims to pave the way and generally contextualize the use of atomistic simulations in the field. In the first part of this thesis, the invaluable AIMD methods using SIESTA code are utilized to support and justify the selection and construction of classical interatomic potentials, where liquid Li, Pb and LLE alloy are systematically investigated. In the second part, a classical potential of the embedded-atom-method class is parametrized for liquid Li using mechanical and non-mechanical properties. A mixing scheme is introduced to reproduce properties of liquid LLE alloy. To minimize the arbitrariness of functional forms, the parametrization schemes address the uniqueness problem. CMD simulations with LAMMPS code are performed to extensively investigate and estimate static and dynamic properties of pure LM and He/LM systems, both bulk and interfacial properties. In the third part, in analogy with recent advances in crystallization and droplet formation studies, the diffusive-shielding stabilization, the thermodynamic irreversibility of bulk nanobubbles (bNBs) mechanisms and the mean-first passage times theory are revisited and deployed to characterize the stability of He-bNBs in liquid LLE alloy, as inspired from bubble stability theories and experiments in closed and finite-volume systems. Namely, an analytical perturbation approach with an appropriate equation-of-state of He-bNBs and a stochastic and kinetic approach via forward CMD simulations are established, and the consistency and equivalency of both is demonstrated and thoroughly discussed. Most importantly, the underlying theoretical bases, assumptions, limitations and their computational counterparts are extensively described and illustrated. The overall proposed framework resolves ambiguities about the influence of the simulation domain and time on the observed bNBs in CMD simulations. Essentially, it provides a novel and plausible explanation for helium-bNBs existence and persistence by carefully assessing and estimating the thermodynamic equilibrium conditions; hence, their stability and longevity are shown not to be in violation of the fundamental laws of solubility and diffusivity, at least in CMD simulations and given the relatively high supersaturation levels. Lastly, thermodynamic and kinetic conditions required for the helium nucleation phenomena to take place at LLE-BBs operating conditions are inferred, based on the various investigated theories and performed computations, and coherently, macroscopic modelling suggestions and recommendations are provided.
  

DOCTORAL DEGREE IN PHOTONICS

• BESLIJA, FARUK: Hybrid diffuse optical monitoring and imaging: New approaches and applications in muscle and brain
  
  Author: BESLIJA, FARUK
  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: 28/10/2025
  Deposit END date: 10/11/2025
  Thesis director: DURDURAN, TURGUT | FERRER URIS, BLAI
  Thesis abstract: The generation of energy in the human body relies on oxygen metabolism, determined by oxygen delivery through blood flow and extraction at the tissue level. Reliable assessment of these parameters is crucial for understanding physiological function and tissue adaptations under various stimuli. Conventional monitoring tools for blood flow and oxygen saturation face trade-offs between cost, portability, and technical limitations (depth, resolution, dynamics), restricting their real-time deep-tissue use.This thesis advances diffuse optics, a non-invasive, safe, scalable approach exploiting light diffusion in scattering media, and introduces methodological and instrumental innovations for monitoring blood flow and oxygenation in adult skeletal muscle and brain—two of the most oxygen-demanding organs.Part I investigated long-term physiological adaptations in forearm muscles of advanced rock climbers versus healthy controls. Rock climbing requires exceptional grip endurance, making it an ideal model for localized neuromuscular and hemodynamic adaptations to chronic training. Two protocols were applied: (1) a resting vascular occlusion test (VOT) combining near-infrared spectroscopy (NIRS, oxygenation) and diffuse correlation spectroscopy (DCS, blood flow), and (2) an intermittent grip endurance test measuring force, NIRS, and electromyography (EMG). Results showed climbers had faster blood flow recovery and higher hemoglobin concentrations after occlusion, indicating enhanced vascular response. During exercise, they maintained force longer and used oxygen more efficiently. However, steady-state measures revealed no significant inter-group differences, suggesting adaptations are demand-driven rather than evident at rest. This study is novel in (1) applying DCS to climbing physiology and (2) integrating mechanical, neuromuscular, and hemodynamic measures in one framework.Part II focused on high-density (HD) cerebral blood flow (CBF) mapping, a key marker of brain metabolism. Current systems are bulky, costly, and clinical-only. We developed a new diffuse optics platform using speckle contrast optical spectroscopy (SCOS) and its tomographic extension (SCOT), leveraging cost-effective CMOS technology to improve signal-to-noise ratio (SNR) and scalability while retaining cortical sensitivity. A fiber-based prototype validated signal quality and flow sensitivity in forearm and forehead tests. Building on this, we designed a full-scale HD-SCOT system, nearing completion, intended for real-time, non-invasive mapping of CBF over large cortical areas (e.g., visual cortex).Final contribution: a proof-of-concept SCOS extension enabling simultaneous blood flow and oxygenation measurement. Using multiple wavelengths, source-detector separations, and exposure times, it offers a simplified alternative to dual NIRS-DCS systems. Preliminary forearm tests confirmed feasibility, suggesting applications in muscle and brain monitoring.In summary, this thesis advances diffuse optical monitoring by developing new instruments and methodologies for deep-tissue hemodynamics. Applications in sport physiology and neuroimaging highlight the potential of multi-modal, high-density optical systems to deepen understanding of oxygen metabolism in naturalistic, real-time contexts, paving the way for broader physiological and clinical applications.
  
• DEMUTH, ALEXANDER: Quantum imaging with optical fibre structures
  
  Author: DEMUTH, ALEXANDER
  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: 04/11/2025
  Deposit END date: 17/11/2025
  Thesis director: PRUNERI, VALERIO
  Thesis abstract: Two-photon quantum correlations are a resource for quantum communication, sensing, and imaging. Coincidence-based quantum imaging leverages spatiotemporal quantum correlations to form images from two-photon coincidences rather than intensity alone. To be useful beyond free-space laboratory setups, such as in access-constrained, realistic settings, quantum correlations must be distributed through compact waveguides while remaining usable after propagation across many spatial modes and at practical rates.This thesis develops methods for waveguide-based quantum imaging by integrating and advancing three main technology components: engineered quantum light sources based on spontaneous parametric down-conversion (SPDC), optical waveguides (including disordered and multicore optical fibre), and detection with single-photon avalanche diode (SPAD) array cameras, through both time-tagging and rolling-delay compensation. We demonstrate and quantify transport of quantum spatiotemporal correlations, and implement quantum imaging in a waveguided geometry using optical fibre. In addition, we show real-time operation with continuous coincidence image frames. The main specific achievements are:- Foundations for correlation transport. We model the propagation of quantum correlated SPDC two-photon states in media (continuous and discrete pictures), identifying measurable signatures of correlation conservation after transport. We develop a protocol that measures the waveguide point-spread function via coincidence imaging with a time-tagging SPAD array and timestamp post-processing. Using these tools, we define metrics to certify and quantify correlation transport through waveguides, and map its dependence on waveguide parameters. - Waveguide-based transport of SPDC correlations We validate transmission of SPDC spatiotemporal correlations through a custom-fabricated transverse Anderson localization optical fibre and a commercially available multicore fibre (MCF), showing high-dimensional, mode-parallel delivery of correlations necessary for coincidence imaging protocols. We inject position anticorrelated photon pairs into the fibres, extract coincidences from SPAD array timestamps, characterize the waveguide point-spread function through the coincidence-imaging protocol and quantify preservation of correlations. - Waveguided quantum ghost imaging. Building on the established quantum correlation distribution, we implement waveguided quantum ghost imaging (QGI) through a MCF using a non-degenerate signal-idler photon pair SPDC source. The idler illuminates the sample through the waveguide and is detected with a single SPAD, while the signal, which has not interacted with the sample, is imaged with spatial resolution on a SPAD array camera. We report image quality and imaging rates, and identify limits of optical resolution. - Real-time quantum ghost imaging. We realize real-time, low-latency QGI (free-space and waveguided) by leveraging in-pixel asynchronous coincidence extraction in the SPAD array camera, eliminating external post-processing latency. Coincidence image frames are produced continuously, supporting live alignment and use.Together, these results provide methods for waveguided, mode-parallel delivery and measurement of quantum correlations using optical fibre transport and SPAD array camera coincidence imaging, and thus extend quantum imaging technology towards practical waveguided settings. This may outline routes to applications in constrained environments, low photon flux regimes, and imaging at unusual wavelengths, potentially enabling new use-cases for quantum imaging, for example in the life sciences.
  

DOCTORAL DEGREE IN SIGNAL THEORY AND COMMUNICATIONS

• TRULLENQUE ORTIZ, MARTÍN: A Cross-Layer Perspective on Radio Resource Management and User Quality of Experience in V2X-Enabled 5G Networks.
  
  Author: TRULLENQUE ORTIZ, MARTÍN
  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: 31/10/2025
  Deposit END date: 13/11/2025
  Thesis director: SALLENT ROIG, JOSE ORIOL | CAMPS MUR, DANIEL
  Thesis abstract: Vehicular communications are expected to be among the main verticals benefiting from the massive rollout of 5G. The millisecond latencies enabled by 5G, combined with network slicing and the global coverage offered by public cellular networks, leverage the conditions to adopt public cellular networks for vehicle-to-everything (V2X) communications. This vertical will introduce new services that generate additional traffic demands on existing radio infrastructure, posing distinct challenges for mobile network operators. From a service perspective, V2X services require both low latency and high reliability. For example, under a periodic exchange position information between vehicles, a single dropped packet can increase the position error estimate and jeopardize road safety. From a network perspective, vehicular services will demand resources differently from generic Internet users, often creating high user densities along highways, where user density is typically [MT1.1]low and radio deployment is not dense. Moreover, vehicle mobility can lead to long periods during which cells serve very few users, followed by periods of high demand that may compromise resource availability, especially during traffic congestion. To this end, this thesis investigates solutions for cell overloads caused by traffic jams when V2X services run over public cellular networks.The first contribution of this work is an in-depth analysis of vehicular mobility based on a dataset of realistic traces, aimed at understanding how traffic congestion drains radio resources. Studying vehicle flows across cells reveals user concentrations at specific road locations, providing opportunities for congestion control mechanisms. The second contribution examines the repeatability of high-load situations in a real 5G deployment. Measurements over a 5G NSA network show that congestion exhibits consistent daily patterns, duration, and load characteristics, motivating deterministic congestion control strategies.Building on these insights, this thesis proposes and implements two complementary approaches: a network-layer mobility load balancing algorithm that diverts vehicles at cell edges to neighbouring cells during traffic jams, and an application-layer mechanism that manages service degradation to control packet loss under overload. Combining both approaches, this thesis has conceived, designed, implemented and evaluated FOM-5G, a framework capable to derive the best congestion control strategy for each cell overload. The framework leverages the use of historical information data to optimize cell congestion responses through experience. Beyond its design and evaluation, the thesis also proposes an architectural integration of FOM-5G within the O-RAN architecture and network exposure API defined in the 5G core. Finally, the framework is validated through simulations using diverse vehicular traces that capture urban congestion scenarios. Results confirm FOM-5G’s ability to distinguish between different congestion events and select the optimal mitigation strategy, demonstrating its effectiveness in ensuring reliable V2X communications over public cellular networks.
  
• YI, TIEYAN: UAV SAR Interferometry: ARBRES-X Data
  
  Author: YI, TIEYAN
  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: 04/11/2025
  Deposit END date: 17/11/2025
  Thesis director: MALLORQUI FRANQUET, JORDI JOAN
  Thesis abstract: Small UAVs are attractive SAR platforms, but their unstable trajectories and imperfect GPS/IMU logs introduce significant motion errors that degrade image quality. The ARBRES-X system employs wide-beam, high-squint acquisitions that favor short-aperture observations, while simultaneously reducing sensitivity to motion errors. This thesis first reviews the fundamentals of SAR imaging and cross-track interferometry, then analyzes the ARBRES-X system characteristics in detail, with particular attention to how short apertures and wide beams affect processing. The accuracy requirements for platform state in SAR imaging and cross-track interferometry are quantified, revealing that off-the-shelf INS solutions are insufficient. To address this gap, a motion-error estimation algorithm is proposed and validated using simulated data. Building on these results, an end-to-end processing framework for SAR imaging and cross-track interferometry is developed and applied to ARBRES-X data. The framework produces highly coherent interferograms, and differential interferograms clearly detect PARC phase changes induced by controlled deformation, in close agreement with theoretical predictions. In addition, a speed optimization method suitable for short-aperture imaging is also demonstrated.
  

DOCTORAL DEGREE IN URBANISM

• MARTÍ ELÍAS, JOAN MARIA: Hidrografies urbanes. Cicle de l’aigua, forma urbana i estructura territorial al la Vall Baixa i al Delta del Llobregat
  
  Author: MARTÍ ELÍAS, JOAN 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 URBANISM
  Department: Department of Urbanism, Territory and Landscape (DUTP)
  Mode: Normal
  Deposit date: 31/10/2025
  Deposit END date: 13/11/2025
  Thesis director: CROSAS ARMENGOL, CARLES
  Thesis abstract: The research dives in the relationship between urban morphology and water cycle from an ecosystemic perspective that integrates physical, territorial, and infrastructural components. The objective is to demonstrate that urban morphology is directly related with hydrological dynamics and that, therefore, it can become a key tool for redefining water management strategies in metropolitan contexts.This dissertation is grounded in the intensive exploration of a specific territory (la Vall Baixa and the Delta del Llobregat) through five successive approaches, addressing the territory from the metropolitan scale down to local urban fabrics. This process constructs a multi-scalar reading that relates the physical, infrastructural, and morphological conditions of the city to its hydrological dynamics.The territorial approach is enriched by a historical and conceptual dimension that reconstructs a genealogy of urban thought in an ecosystem perspective, based on figures and projects that conceived the city as a living system, dependent on its exchanges with the territory. The research also revisits nineteenth-century projects that, with a hygienist outlook, anticipated “proto-ecological” visions. In this regard, Garcia Fària’s proposal for the sanitation of the Eixample and the diversion of the river Llobregat constitutes a paradigmatic case that anticipates an integrated territorial interpretation, establishing a valuable precedent for contemporary urban planning practice.On this basis, the final stretch of the river Llobregat is studied, revealing the complexity of a hybrid morphology resulting from the superimposition of natural and anthropic logics. The river is presented as an ecological and productive infrastructure, a space where the persistence of hydraulic and agricultural traces coexists with metropolitan urbanization pressures. From here, the research turns to the underground dimension, analyzing the delta aquifer as a key element for environmental balance. Infiltration dynamics, alterations derived from urbanization, and opportunities for the reconversion of industrial and logistical spaces based on hydrogeological criteria are examined. The three-dimensional reading of the territory makes it possible to link surface and subsoil and to propose infiltration and regulation devices that restore natural functions.In the central part of the dissertation, these criteria are applied at an intermediate analytical scale, proposing the sub-basin as a functional unit of analysis and design, due to its capacity to integrate topographical continuities, road structures, and open spaces. On this basis, a parametric methodology is developed, articulated into six actions (capture, consumption, treatment, infiltration, retention, and reuse) applied to multiple metropolitan fabrics. A multi-scalar analysis makes it possible to identify hydromorphological indicators that guide design decisions according to context and urban structure. The result is an operative atlas that proposes ranges of intervention and adaptive criteria for urban transformation guided by hydrological principles.Finally, the research advances a paradigm shift: water becomes a design vector, not merely a technical constraint. Public space, the road network, and urban voids are recognized as multifunctional water devices, while the underground dimension is considered an active and inseparable layer of the urban system. This three-dimensional and complex vision relates metabolism and form, while articulating both regional scale and urban project.Overall, the dissertation contributes to creating a culture of urban design that integrates water as a vector of intelligibility and ordering, where the term “hidrografia” is not understood in the traditional sense of describing river courses, but rather as a critical instrument for mapping the presence, movement, and structuring capacity of water in all its forms.