Public display of deposited theses

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 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
  Deposit END date: 03/11/2025
  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.
  

DOCTORAL DEGREE IN COMPUTER ARCHITECTURE

• OLIVER SEGURA, JOSÉ: Accelerating SpMV on HBM-equipped FPGAs: Hardware-Software Co-design and Collaboration
  
  Author: OLIVER SEGURA, JOSÉ
  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: 27/10/2025
  Deposit END date: 07/11/2025
  Thesis director: AYGUADÉ PARRA, EDUARD | MARTORELL BOFILL, XAVIER
  Thesis abstract: SpMV is a key linear algebra kernel at the core of many algorithms across multiple knowledge domains. Its memory-bound nature and its low arithmetic intensity make its efficient implementation a challenging problem. Usual mechanisms present in general-purpose microprocessors, such as cache memories, become useless without further data transformation as the size of the problem grows beyond the capacity of the cache. The capability of FPGAs to generate application-specific logic and memory hierarchies results in performant and energy-efficient designs. This has made them an interesting alternative when trying to efficiently implement SpMV. The push by vendors to position them as HPC accelerators and the inclusion of HBM in the last generations of boards have increased this trend. Most SpMV implementations for FPGAs allow to work exclusively using single-precision floating-point arithmetic, while in the context of HPC applications, double-precision floating-point arithmetic is usually required. CSR or slightly modified versions of it are usually used as the basis for these implementations. This limits inter and intra-row parallelism due to conflicts in memory accesses, requiring the implementation to include complex logic such as arbitration or stall/retry mechanisms or to use replicated memories, increasing resource usage and limiting the scalability of the designs. This thesis presents two proposals to leverage the features offered by FPGAs, especially HBM and customizable memory hierarchies, to further improve the achieved performance and, in the case of the second proposal, allowing for a precision-agnostic design that can be synthesized to work with different arithmetic types as required.The first proposal consists of a double-precision FPGA co-designed SpMV accelerator and matrix representation. Instead of using CSR as the basis, the representation and the accelerator are defined considering all the advanced features that FPGAs offer, in a co-design approach. This approach allows maximization of inter-row and intra-row parallelism by allowing simultaneous processing of several matrix values per cycle in a fully pipelined fashion without requiring complex logic or memory replication. The proposed matrix representation allows the easy partitioning of work among different accelerators and the efficient use of HBM bandwidth. The evaluation shows that the proposed implementation outperforms state-of-the-art implementations in terms of absolute, bandwidth-relative, and energy-relative performance.The second proposal builds on the first one, increasing its arithmetic efficiency. It does so in different ways. In the first place, it improves the efficiency of the proposed encoding by reducing the amount of metadata required to process the matrix. In the second place, it increases the useful data ratio of the transformed representation by considering new hierarchical abstractions within the matrix. In the third place, it repurposes zero-padding, when present, to act as a carrier of useful data. This proposal is highly parametrizable, including the possibility of using it to generate designs working with different data types without requiring more changes than setting the desired data type at compile time. The evaluation shows that this proposal significantly improves over the first one in double-precision arithmetic. Single-precision results demonstrate its capability to improve the performance offered by state-of-the-art designs that use much higher bandwidth.
  

DOCTORAL DEGREE IN COMPUTING

• PONTÓN MARTINEZ, JOSE LUIS: Learning Data-driven Character Animation for Avatars in Virtual Reality
  
  Author: PONTÓN MARTINEZ, JOSE LUIS
  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 COMPUTING
  Department: Department of Computer Science (CS)
  Mode: Normal
  Deposit date: 27/10/2025
  Deposit END date: 07/11/2025
  Thesis director: ANDUJAR GRAN, CARLOS ANTONIO | PELECHANO GOMEZ, NURIA
  Thesis abstract: The accelerating trend of remote interaction, driven by globalization and digital communication, underscores the need for richer, more immersive virtual collaboration. While current 2D video platforms enhance communication, Virtual Reality (VR) offers the unique potential for truly natural 3D interaction. Accomplishing this, however, critically depends on accurately representing human motion and achieving presence within virtual environments.This thesis addresses the challenge of achieving real-time, high-fidelity, and perceptually natural full-body self-avatar animation within VR environments using consumer-grade tracking devices. Accurate self-avatars are fundamental for inducing a strong Sense of Embodiment and enabling effective non-verbal communication, yet current methods often struggle with the inherent sparsity and variability of available sensor data.We first address fundamental aspects of animation fidelity and perceptual realism, and introduce methodologies for precise avatar skeleton adjustment, which significantly mitigate issues arising from mismatches between a user's physical proportions and their virtual representation. We also study various interaction metaphors to minimize visual discrepancies between real controllers and virtual hands, thereby enhancing user embodiment and task performance. These studies underscore the importance of accurate animation and lay the groundwork for learning-based approaches to achieve natural and temporally coherent motion from sparse inputs, overcoming the limitations of traditional inverse kinematics.Building upon these insights, the thesis explores the development of data-driven reconstruction methods that can handle diverse and ambiguous sensor inputs. We propose a novel deep learning-based system that accurately reconstructs full-body poses from minimal consumer-grade VR trackers, effectively addressing the underdetermined nature of this problem. Recognizing the inherent one-to-many mapping problem in sparse input, where a single input can correspond to multiple plausible poses, we then explore the potential of generative AI. Our work demonstrates how Variational Autoencoders (VAEs) can enable fine-grained control and adaptability to variable sensor configurations through latent space optimization, while diffusion models facilitate multimodal reconstruction from novel sensor types, such as pressure-sensing insoles.
  

DOCTORAL DEGREE IN CONSTRUCTION ENGINEERING

• 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
  Deposit END date: 04/11/2025
  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.
  

DOCTORAL DEGREE IN ELECTRONIC ENGINEERING

• 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
  Deposit END date: 04/11/2025
  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
  Deposit END date: 04/11/2025
  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
  Deposit END date: 05/11/2025
  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 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
  Deposit END date: 04/11/2025
  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.
  

DOCTORAL DEGREE IN MATERIALS SCIENCE AND ENGINEERING

• 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
  Deposit END date: 03/11/2025
  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 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.
  
• 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
  Deposit END date: 03/11/2025
  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.
  
• 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 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.
  

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
  Deposit END date: 03/11/2025
  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.