Theses authorised for defence

DOCTORAL DEGREE IN APPLIED MATHEMATICS

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

DOCTORAL DEGREE IN ARCHITECTURAL, BUILDING CONSTRUCTION AND URBANISM TECHNOLOGY

• ARIAS CUEVAS, JOSÉ GABRIEL: Proyectos de recuperación de zonas vulnerables con materiales de ciclo cerrado. Casos de estudio, proyectos URBE.
  
  Author: ARIAS CUEVAS, JOSÉ GABRIEL
  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: 05/03/2026
  Reading date: pending
  Reading time: pending
  Reading place: pending
  Thesis director: BOSCH GONZÁLEZ, MONTSERRAT
  Thesis abstract: The Dominican Republic, specifically Santo Domingo, has faced decades of unplanned urbanization in highly vulnerable areas, such as the banks of the Ozama River. This generates a socio-environmental risk intensified by overpopulation and a lack of specific public policies. Simultaneously, the absence of an integrated system for managing Construction and Demolition Waste (CDW), often disposed of improperly, causes critical environmental impacts. This research addresses the integration of CDW management and closed-loop materials into urban redevelopment projects promoted by the State in critical areas. Using La Nueva Barquita and Domingo Savio (URBE Projects) as case studies, the research serves as a starting point for future interventions in the country.The central objective is to propose construction alternatives to existing ones for urbanization projects in flood-prone areas through the systematic use of closed-loop materials and CDW recovery and valorization systems. It seeks to provide strategic knowledge so that these urban interventions can become "waste sinks," driving sustainable, socially committed, and viable development.The research follows a mixed methodology combining: a documentary study and critical analysis of local regulations; international references; and the state of the art regarding vulnerability and river intervention projects. It includes the analysis of the case study settlements, the construction sector and its main stakeholders, and the regulatory framework, alongside successful experiences of urban interventions using recycled products. Field research involved site visits and surveys of both residents of the Nueva Barquita project and key stakeholders in Santo Domingo's construction sector. Finally, a technical-economic analysis of work items was conducted using quantification tools such as TCQ/BEDEC. This triple approach allowed for a comprehensive understanding of local barriers and opportunities.The results are framed within three transformation vectors:Regulatory/Institutional Vector: Although a legal framework exists, technical instruments and specific contractual clauses are needed to integrate CDW management as a mandatory requirement in public works.Socio-Economic Vector: The research identified an active informal reuse market, demonstrating latent demand. Formalizing this sector can generate a new competitive and formalized economic fabric.Technical/Territorial Vector: The real feasibility of replacing conventional materials with CDW in redevelopment projects is validated, optimizing resilience on riverbanks through solutions combined with nature-based solutions (NBS).The findings demonstrate that while the Dominican State has a visible social commitment to relocating vulnerable populations, a critical gap persists in regulatory application and technical CDW integration. The main contribution of this thesis lies in having integrated a technical, regulatory, and territorial diagnosis that validates the hypothesis that urban projects can function as "waste sinks" in the Dominican context.The research establishes technical, institutional, and market foundations for the Dominican Republic to adopt a circular economy model in construction. The sector's robust growth presents an exceptional opportunity to implement this model, attracting sustainable investment and strengthening international competitiveness. This transforms the waste challenge into a strategic lever for resilient urban development and national economic prosperity.
  
• GONZÁLEZ SÁNCHEZ, BELÉN: Pavimentos de yeso: de la tradición al presente
  
  Author: GONZÁLEZ SÁNCHEZ, BELÉN
  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: 27/01/2026
  Reading date: 27/03/2026
  Reading time: 12:30
  Reading place: EPSEB (Escuela Politécnica Superior de Edificación BCN) - Sala de GradosAv. Doctor Marañón, 44-50 - 08028 - Barcelona
  Thesis director: ROSELL AMIGÓ, JUAN RAMON | NAVARRO EZQUERRA, MARIA ANTONIA
  Thesis abstract: Traditional architecture is characterised by construction techniques that are adapted to the geographical environment and use locally sourced materials. In the eastern half of the Iberian Peninsula, gypsum is one of the most abundant and versatile materials used in the development of the region's heritage. It comes in many forms and has many uses, giving rise to simple yet nuanced architecture. Despite gypsum's humble status as a traditional material, its adaptability has enabled it to be employed in various construction systems, including the creation of vaulted ceilings, interior staircases, walls, interior and exterior cladding, pavements, decorations, cornice mouldings, etc. However, in 20th-century and early 21st-century construction, there seems to be no place for this important building tradition, and its use has taken a back seat to new construction systems and industrialised materials. Currently, the most widespread use of gypsum, as a binder, is linked to the production of prefabricated elements and the creation of interior cladding and decorations. Of all the traditional construction systems where the use of gypsum as a binder has been documented, continuous or discontinuous pavements are the least documented or recognized. Throughout history, there are few references to the use of gypsum pavements, some of them vague, which show its use over the centuries and across civilizations, but without representing a finding of great importance within the monumental complex.This doctoral thesis aims to deepen our understanding of how traditional gypsum is obtained by studying the process of transforming gypsum stone through calcination both theoretically and practically. The aim is to propose a constructive solution that is compatible with traditional systems for the continuous production of gypsum pavements. The methodology is based on an extensive literature review covering the transformation and production of traditional gypsum binders from gypsum stone, the addition of additives to gypsum binders and their use in paving. The review also covers the characteristics and performance requirements currently demanded of continuous pavements. Two experimental campaigns have been carried out based on this documentary research. The first, which was conducted in the field, provided a better understanding of the processes involved in transforming stone into a binder in real-life situations by monitoring four experimental gypsum kilns in Catalonia and Aragon. This work has demonstrated that stone is fired at temperatures ranging from 800 to 1000°C, although these values may occasionally be exceeded. The second campaign, which was developed in a laboratory, has made it possible to produce a continuous gypsum pavement using an experimental prototype. This pavement is compatible with traditional systems, constructively viable with commercial materials, and compliant with current regulatory requirements.
  

DOCTORAL DEGREE IN AUTOMATIC CONTROL, ROBOTICS AND VISION

• AGUILAR PLAZAOLA, JOSÉ AGUSTÍN: DATA-DRIVEN MODELLING, STATE ESTIMATION, CELL CONTROL AND MOTION PLANNING FOR PEM FUEL CELL-POWERED VEHICLES
  
  Author: AGUILAR PLAZAOLA, JOSÉ AGUSTÍN
  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: 05/03/2026
  Reading date: pending
  Reading time: pending
  Reading place: pending
  Thesis director: HUSAR, ATTILA PETER | ANDRADE CETTO, JUAN
  Thesis abstract: This doctoral thesis presents novel advances in the areas of modeling, state estimation, path planning, and control to improve energy efficiency and durability of the powertrain of autonomous robots and electric vehicles driven by proton exchange membrane fuel cells. The main objective of the present work is to design and implement algorithms that, based on a thorough knowledge of the systems in question, improve the characteristics and outperform the state-of-the-art methods. Special emphasis is placed on testing the developed algorithms, as much as possible, with dynamic experimental profile dataIn the area of proton-exchange membrane (PEM) fuel cell modelling, a computationally efficient physical model is proposed. Next, a model with a structure based on neural networks, built exclusively from data, is developed and validated. This model is framed within a new paradigm of machine learning, the computation by reservoirs. Subsequently, a hybrid model is built, combining both the physical model and the data-driven model by means of a fusion algorithm based on radial basis functions. The three models are tested with a set of dynamic experimental data, and it is shown how the proposed hybrid structure outperforms each of the individual models.In the area of state estimation, a particle filter is developed with the objective of estimating internal states (or parameters) of the fuel cell, taking into account the nonlinearity of the system and the uncertainty in its model. The algorithm is capable of estimating the internal variables of a nonlinear system with non-Gaussian probabilistic distribution. The algorithm is implemented to estimate the exchange current density of a fuel cell and is tested with two sets of experimental data, outperforming two state-of-the-art estimation algorithms. The exchange current density estimation is then used to fit an auto-regressive model and predict the evolution of the stack voltage in a durability experiment.In the area of PEM fuel cell control, an architecture composed of a high-level controller is proposed, which is in charge of calculating optimal temperature values with the objective of minimizing the degradation of the catalyst layer of the PEM fuel cell and at the same time maximizing its performance. These optimum values are then sent to the local controller of the fuel cell temperature regulation system. The proposed controller is based on the model predictive control paradigm; for this, a multiobjective cost function is designed, based on state-of-the-art models of the platinum degradation process that occurs during stack operation. The controller is validated in simulation tests and shown how it can adapt the temperature according to load conditions, optimizing the performance of the catalyst layer and minimizing its degradation.In the area of path planning, a new planning algorithm is developed taking into consideration the degradation mechanisms in the catalyst caused by the cell voltage profile. The developed algorithm is an extension of the A* algorithm, including new cost and heuristic functions based on the latest degradation models available in the literature. These functions incorporate penalties related to the expected voltage profile in the routes that are more detrimental to the catalyst integrity. Simulation tests are performed with different scenarios and the performance of the developed path planner is compared with the conventional A* algorithm.In the area of energy efficiency control, a controller is developed with the objective of including energy optimization in an adaptive cruise control module. Each part of the controller is designed, including the system model, the cost function, and the constraints. A series of simulation tests are performed to compare the performance between the energy-optimized adaptive cruise controller and the conventional one.
  

DOCTORAL DEGREE IN BIOMEDICAL ENGINEERING

• FUENTES LLANOS, JUDITH: Development of 3D Bioengineered Skeletal Muscle-based Bioactuators for Biorobotic and Biomedical Applications
  
  Author: FUENTES LLANOS, JUDITH
  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: Article-based thesis
  Deposit date: 11/02/2026
  Reading date: 10/04/2026
  Reading time: 11:00
  Reading place: Sala d'Actes, Edifici Vèrtex, Campus Diagonal Nord, Vèrtex (VX), Plaça d'Eusebi Güell, 6, 08034 Barcelona
  Thesis director: SÁNCHEZ ORDÓÑEZ, SAMUEL | GUIX NOGUERA, MARIA
  Thesis abstract: Biohybrid systems are engineered constructs that integrate living materials, like cells or tissues, with synthetic components, such as electronics, mechanical structures, or other artificial materials. This integration aims to leverage the unique capabilities of biological materials, including self-assembly, responsiveness to certain stimuli, adaptability, and self-repair, being features challenging to replicate in their synthetic counterparts. By combining these traits with the robustness and compliance of synthetic structures, biohybrid systems emerge as versatile platforms capable to both adapt and actuate in dynamic environments. Among the various biological components explored, skeletal muscle stands out due to its high force-to-weight ratio, controllable contraction, and compatibility with 3D biofabrication strategies, making it ideal for applications in soft robotics, drug testing, and regenerative medicine.This PhD thesis focuses on the development of 3D bioengineered skeletal muscle bioactuators, addressing key aspects from the biofabrication to functionality improvement, actuation control, and regenerative capacity.We developed and optimized a Pluronic-Assisted Coaxial 3D bioprinting (PACA-3D) method capable of biofabricating fascicle-like skeletal muscle bioactuators with improved maturation, sarcomere formation and contractile force when compared to muscles generated using conventional extrusion-based 3D bioprinting. To improve actuation and directional control, we introduced magnetic responsiveness into the bioactuators by developing a biocompatible ferrofluid-based bioink, used to fabricate the Ferromuscle, in collaboration with Waterloo and Aalto Universities. Such bioactuators exhibited improved cell alignment and force output, as well as magnetically guided actuation. In collaboration with the University of Cagliari (UniCa), we also evaluated the integration of flexible Organic Field-Effect Transistor (OFET)-based strain sensors for real-time monitoring of contractile behavior. These devices allowed tunable force sensing, showing no signal cross-talk when using standard electrical stimulation protocols, which is paving the way for closed-loop stimulation strategies and automated drug screening platforms. Moreover, we evaluated the self-healing capacity of the bioactuators after damage conditions, designed to mimic the mechanical stress that they typically undergo during manipulation of biohybrid systems. We observed partial to full force recovery and structural remodeling without external interventions, although the underlying biological mechanisms remain to be elucidated.Finally, additional collaborative work that enriched this thesis will be also discussed. Such studies include the integration of tendon-like structures to develop muscle-tendon unit (MTU) actuators with enhanced biomimicry, performance, and stability (ETH Zurich). Moreover, it will also be covered the design of biohybrid flexure mechanisms powered by skeletal muscle, evaluating how skeleton architecture and voltage- or current-based stimulation influence actuation output (SSA and UniCa).Altogether, the work presented in this thesis contributes to the field of biohybrid robotics by combining advanced fabrication, actuation control, integrated sensing, and regenerative capacity. These developments move us closer to robust, autonomous, and functional biohybrid machines for applications in soft robotics, tissue engineering, and biomedical research.
  
• PRATS BISBE, ALBA: Nous entorns interactius de realitat virtual immersiva aplicats a la neurorehabilitació de funcions cognitives i sensoriomotores
  
  Author: PRATS BISBE, ALBA
  Thesis file: (contact the Doctoral School to confirm you have a valid doctoral degree and to get the link to the thesis)
  Programme: DOCTORAL DEGREE IN BIOMEDICAL ENGINEERING
  Department: Department of Automatic Control (ESAII)
  Mode: Normal
  Deposit date: 29/01/2026
  Reading date: 08/04/2026
  Reading time: 12:00
  Reading place: Sala d'Actes de l'Institut Guttmann (Carretera de Can Ruti, s/n, 08916 Badalona, Barcelona)
  Thesis director: JANE CAMPOS, RAIMON | OPISSO SALLERAS, ELOY
  Thesis abstract: Recovery following an acquired brain injury (ABI) represents a major challenge with significant implications for health, quality of life, and socioeconomic burden. Advances in medicine and neurorehabilitation —particularly personalized, patient-centred clinical practice— have contributed to improved outcomes by promoting autonomy and participation in society. Technology has become a cornerstone in this continuous improvement, and virtual reality (VR) has emerged as a promising therapeutic tool. VR immersive properties, ergonomic interaction capabilities, ecological validity, and intrinsic advantages of digitalisation can enhance motor learning and cognitive improvement. However, there is still no conclusive evidence regarding VR clinically significant effects and consistent adoption in routine hospital practice.The aim of this thesis is to identify the barriers to VR implementation in neurorehabilitation and to elicit the specific requirements for its integration as an effective support tool. To achieve this goal, a systematic review of current applications was first conducted to identify methodologies and features associated with significant therapeutic outcomes. This analysis revealed considerable heterogeneity across clinical protocols, as well as the hardware and software used, highlighting the lack of standards and quality criteria that hinder result generalization. To address this gap, a new conceptual framework for evaluating VR tools in clinical contexts was developed: the Virtual Reality-tools Quality Assessment Framework (VR-tools QAF), which defines technical, functional, and safety requirements for VR equipment and virtual environments. In parallel, a comprehensive methodology for user-centred design, iterative development, and clinical validation was created, termed the Virtual Reality-tools Design and Development Guide (VR-tools DDG).Through the combined model (VR-tools QAF + DDG), eleven multimodal VR environments were developed to support the rehabilitation of cognitive and sensorimotor functions in individuals who have suffered an ABI. These environments integrate principles of repetitive practice, multisensory feedback, adaptive difficulty, and mechanisms for tailoring the experience to the patient’s profile. Validation was carried out in collaboration with a leading hospital in neurorehabilitation and brain health, where usability and feasibility tests were conducted with healthcare professionals and patients with ABI. Proof-of-concept trials demonstrated good acceptance and ease of use among clinicians (n = 26) from different specialties, as well as good tolerance and absence of adverse effects among the 20 patients. Moreover, a longitudinal VR intervention conducted within an efficacy study confirmed the feasibility and safety of delivering a 9-hour VR-based cognitive rehabilitation protocol with 21 patients with ABI, distributed in 20–30-minute sessions, 2–3 times per week.Overall, this research establishes a comprehensive methodological model for effectively integrating immersive VR into neurorehabilitation hospital settings, combining scientific rigour with technical and clinical feasibility. The results lay a solid foundation for future efficacy studies aimed at developing standardized treatment protocols for patients with ABI.
  

DOCTORAL DEGREE IN COMPUTER ARCHITECTURE

• DOBLAS FONT, MAX: High-Performance Sequence Alignment: Co-Designing Algorithms and Hardware Architectures for Efficient and Scalable Acceleration
  
  Author: DOBLAS FONT, 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 COMPUTER ARCHITECTURE
  Department: Department of Computer Architecture (DAC)
  Mode: Normal
  Deposit date: 20/02/2026
  Reading date: 07/04/2026
  Reading time: 16:00
  Reading place: C6-E106
  Thesis director: MORETÓ PLANAS, MIQUEL | MARCO SOLA, SANTIAGO
  Thesis abstract: Over the past decade, the exponential growth of genomic data has driven significant advancements in genomics and healthcare, enabling breakthroughs in large-scale genomic studies, personalized medicine, and epidemiological surveillance. However, this rapid data expansion has also posed substantial computational challenges, particularly in sequence alignment, a cornerstone of genomic analysis. Sequence alignment is critical for applications such as disease diagnostics, population-wide genetic research, and outbreak tracking, yet it demands scalable and efficient solutions to handle the increasing data volumes and diverse use cases.This thesis addresses the efficiency-flexibility gap in sequence alignment through three key contributions. First, it introduces QuickEd, a novel sequence alignment algorithm that reduces computational complexity by combining heuristic bounding with optimal alignment. QuickEd achieves O(nŝ) complexity, where n is the sequence length and ŝ is an estimated upper bound of the alignment score, significantly improving upon the O(n²) complexity of traditional dynamic programming algorithms. By efficiently bounding the maximum alignment score, QuickEd reduces the computational burden while maintaining accuracy, making it well-suited for long-read sequencing and other demanding applications.Second, this thesis proposes GMX, a set of instruction set architecture (ISA) extensions designed to enhance the efficiency of dynamic programming-based alignment algorithms. GMX provides fundamental building-block operations for fast, tile-wise computations of the DP matrix, reducing memory footprint and computational overhead. These extensions enable seamless integration into widely used algorithms and tools, offering a cost-effective alternative to domain-specific accelerators (DSAs) while achieving comparable performance improvements.Third, the thesis presents SMX, a heterogeneous architecture that balances flexibility and performance to address the diverse requirements of real-world sequence alignment tasks. SMX integrates an ISA extension (SMX-1D) for irregular and sequential tasks and a specialized coprocessor (SMX-2D) for regular and parallel tasks, all orchestrated by a general-purpose core. This architecture supports various configurations for different sequence types (DNA, protein, and ASCII text) and alignment models, including weighted gaps and substitution matrices. By combining high performance with adaptability, SMX enables efficient acceleration of a wide range of sequence alignment applications.Together, these contributions advance the state of the art in sequence alignment, providing scalable, flexible, and efficient solutions to meet the demands of modern genomic analysis. The innovations presented in this thesis pave the way for faster and more reliable genomic analyses, facilitating critical applications such as personalized medicine, population-scale sequencing, and epidemiological surveillance in the era of long-read sequencing technologies.
  
• VALDÉS JIMÉNEZ, ALEJANDRO MAURICIO: Design, parallelization and acceleration of algorithms to discover three-dimensional patterns in proteins
  
  Author: VALDÉS JIMÉNEZ, ALEJANDRO MAURICIO
  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: 03/03/2026
  Reading date: 09/04/2026
  Reading time: 15:00
  Reading place: C6-E101
  Thesis director: JIMENEZ GONZALEZ, DANIEL | NUÑEZ VIVANCO, GABRIEL
  Thesis abstract: The rapid growth of protein structure databases, such as the Protein Data Bank (over 230,000 structures) and AlphaFold (over 200 million structures), requires efficient and scalable algorithms capable of exploiting high-performance computing (HPC) architectures to enable large-scale structural analysis in reasonable times. This thesis focuses on the design and implementation of optimized and parallel algorithms for discovering, analyzing, and clustering conserved three-dimensional amino acid patterns in proteins. The work focuses on the Geomfinder algorithm (A multi-feature identifier of similar three-dimensional protein patterns: a ligand-independent approach), which compares three-dimensional patterns between pairs of proteins, and the novel 3D-PP algorithm (A tool for discovering conserved three-dimensional protein patterns), proposed in this thesis, which discovers and clusters common three-dimensional patterns within protein sets. Both algorithms are ligand and sequence independent and do not require predefined patterns, enabling the identification of previously unknown functional sites. However, their original sequential implementations limit their applicability to large datasets. For Geomfinder, several sequential optimizations were introduced to reduce algorithmic complexity and long-latency operations. The incorporation of a Merge Join–based strategy reduced partial scoring complexity from O(N×M) to O(N+M), ensuring each descriptor element is evaluated only once. Lazy evaluation and reordering of partial scoring function calls further reduced execution time. These optimizations achieved speedups ranging from 6.2x to 19.7x, depending on the search range. Multiple parallelization strategies were then explored, including OpenMP, MPI, hybrid MPI+OpenMP, and CUDA. OpenMP with fine-grained data decomposition and optimized scheduling achieved near-ideal speedups, reaching 32.6x with 64 threads. MPI-based distributed parallelization achieved up to 19.4x speedup with 64 processes, while hybrid MPI+OpenMP further improved performance, reaching 67.4x using 1,024 threads. GPU acceleration using CUDA provided speedups of up to 8.6x, with performance increasing for larger workloads. After applying the algorithmic optimizations to the original sequential version, profiling revealed a change in the computational bottleneck, and an additional OpenMP parallelization stage was applied, achieving up to 494x acceleration over the original sequential version. In one case study, the runtime was reduced from over one hour to approximately 3.4 seconds. For the 3D-PP algorithm, profiling revealed that over 96% of execution time was spent processing protein chains. All major components were parallelized. Three OpenMP approaches were evaluated, with the best solution based on explicit and nested tasks, achieving a 22.3x speedup and reducing execution time from 1.25 hours to 201.5 seconds. Distributed-memory strategies using MPI focused on minimizing communication through early pattern reduction, achieving speedups of approximately 32x with 64 processes. Hybrid MPI+OpenMP implementations further improved performance, with the best approach achieving a 162.5x speedup and reducing runtime to 27 seconds. This hybrid approach mitigated synchronization overhead inherent to pure OpenMP implementations and demonstrated weak-scaling efficiency (90-100%) up to 8 processes, although efficiency dropped to around 72% when using 16 processes due to increased load imbalance and synchronization costs. The results show that explicit task parallelism and early data reduction substantially improve the performance and scalability of 3D-PP. All these improvements will help us address processing and pattern discovery in larger protein databases.
  

DOCTORAL DEGREE IN ENVIRONMENTAL ENGINEERING

• CORREA GONZÁLEZ, SANDRA: Anaerobic digestion-based biorefineries to advance circularity in the olive oil sector
  
  Author: CORREA GONZÁLEZ, SANDRA
  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: 27/02/2026
  Reading date: 21/04/2026
  Reading time: 10:30
  Reading place: Place: ETSECCPBUPC, Campus NordBuilding C1. Classroom: 002C/Jordi Girona, 1-308034 Barcelona
  Thesis director: FERRER MARTI, IVET | PASSOS LOPES, FABIANA
  Thesis abstract: Every year, the olive oil industry generates large quantities of olive pomace, a by-product that currently represents an environmental problem but has great potential for the recovery of bio-based products and bioenergy within a biorefinery framework.In this context, the objective of this PhD thesis was to develop and evaluate anaerobic digestion–based biorefinery strategies for the valorisation of olive pomace. Specifically, the limitations of olive pomace mono-digestion were assessed, and co-digestion was investigated as a strategy to improve process stability and methane production. Biorefinery models for the recovery of bio-based products and biogas were examined, the agronomic potential of the resulting digestates was analysed, and last anaerobic fermentation was explored as an alternative valorisation pathway.The results demonstrated that olive pomace mono-digestion is unstable due to substrate-related properties, including acidic pH, lack of alkalinity and nutrients, high C/N ratio, and the presence of phenolic compounds. These limitations led to the accumulation of volatile fatty acids, inhibition of methanogenesis, and collapse of the microbial community, resulting in very low methane yields.Co-digestion with nitrogen-rich co-substrates, specifically from the swine farming sector, proved to be an effective strategy to overcome these limitations. In particular, pig slurry provided alkalinity, nutrients, and water, enabling stable operation, increased microbial diversity, and enhanced methane yields (145 mL CH₄·g⁻¹ VS). In contrast, co-digestion with pig manure achieved higher methane yields (289 mL CH₄·g⁻¹ VS) but exhibited lower resilience under stress conditions.Two anaerobic digestion–based biorefinery models were developed in this thesis. In the first, an ionic liquid pre-treatment ([Et₃NH][HSO₄], 120 °C, 1 h) was applied to recover lignin nanoparticles from olive pomace, followed by anaerobic digestion of the residual fraction. Analysis of the residual olive pomace revealed a less compact surface structure and greater bioaccessibility for microorganisms, resulting in higher methane yields during co-digestion with pig slurry compared to untreated olive pomace (173 mL CH₄·g⁻¹ VS). In the second biorefinery model, a thermal process (water, 100 °C, 45 min) was used to recover natural dyes for textile applications. The residual olive pomace fraction retained 88% of the initial methane potential. During semi-continuous reactor operation, co-digestion of this fraction with pig slurry achieved a methane yield of 157 mL CH₄·g⁻¹ VS.Digestates derived from the co-digestion of untreated and residual olive pomace with pig slurry exhibited favourable agronomic properties, such as near-neutral pH, balanced nutrient content, and partially stabilised organic matter. However, phytotoxicity at high application rates and elevated Hg concentrations highlight the limitations of these digestates and the need to blend them with other organic fertilisers to ensure safe soil application.Anaerobic fermentation was identified as an alternative or complementary pathway for olive pomace valorisation. Temperature and hydraulic retention time strongly influenced volatile fatty acid yields and profiles, as well as microbial community composition.Overall, this thesis demonstrates the potential of anaerobic digestion–based biorefineries for the valorisation of olive pomace and the production of value-added bio-based products, renewable energy, and organic fertilisers, contributing to the development of circular and resilient agro-industrial systems.
  
• SOUSSE VILLA, RUBEN: Dust in the atmosphere: Integrated modeling of heterogeneous chemistry, mineralogy, and optical properties
  
  Author: SOUSSE VILLA, RUBEN
  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: 16/02/2026
  Reading date: 29/04/2026
  Reading time: 12:00
  Reading place: Place: ETSECCPBUPC, Campus NordBuilding C1. Classroom: 002C/Jordi Girona, 1-308034 Barcelona
  Thesis director: PEREZ GARCIA-PANDO, CARLOS | JORBA CASELLAS, ORIOL
  Thesis abstract: Airborne mineral dust is one of the dominant aerosol components in the atmosphere and plays a central role in the Earth system through its interactions with atmospheric composition and radiation. Despite its importance, large uncertainties remain in its representation in atmospheric and climate models, particularly concerning dust heterogeneous chemistry. These reactions between dust particles and gas species play a dominant role over dust affected regions in the formation of secondary inorganic aerosols such as particulate nitrate, ammonium, and sulfate. Yet, they are still subject to strong simplifications and parameterizations in atmospheric models, which still show significant discrepancies in their representation.This Thesis aims to advance the understanding of dust atmospheric chemical processes, their dependence on mineralogical composition, and their implications for aerosol optical properties. To this end, multiple dust heterogeneous chemistry mechanisms and mineralogical representations are implemented and systematically evaluated within the MONARCH atmospheric chemistry model. Their impacts on atmospheric composition, aerosol acidity, and optical properties are assessed and evaluated against observations. Notably, this work represents the first integration into an atmospheric chemistry model of observationally constrained maps of surface mineral composition of arid surfaces derived from the recently launched NASA’s Earth Surface Mineral Dust Source Investigation (EMIT) spectrometer.The evaluation of different dust heterogeneous chemistry mechanisms highlights the importance of representing reversible secondary inorganic aerosol formation in both fine and coarse particle modes, together with an accurate treatment of dust and sea-salt alkalinity. This combined approach is shown to be necessary to achieve consistent nitrogen budgets and aerosol acidity, and to better reproduce observed atmospheric nitrate concentrations.The comparison of different mineral atlases focuses on calcite as the primary contributor to dust alkalinity, demonstrating that EMIT provides the most consistent representation of the atmospheric calcite cycle. The use of source-resolved mineralogy compared to the common assumption of globally averaged dust mineral content, reveals strong geographical contrasts in calcite abundance, particularly between the Middle East and other major dust regions such as the Sahara and East Asia. These differences in calcite lead to marked regional and size-dependent implications for aerosol acidity and secondary inorganic aerosol formation. Impacts on particulate nitrate are found to be most pronounced during short-lived, intense formation events in regions affected by both dust and anthropogenic pollution, while global burdens and long-term trends are less sensitive.The implications of dust chemistry and mineralogical representation for aerosol optical depth and single scattering albedo are generally modest, with fine-mode aerosols exerting a larger influence than coarse particles, though both remain secondary compared to strongly absorbing minerals such as iron oxides. Limitations related to the representation of absorbing carbonaceous aerosols and the absence of cloud–aerosol interactions constrain the assessment of radiative effects.Overall, this Thesis demonstrates that combining reversible dust heterogeneous chemistry with source-resolved mineralogical information from EMIT substantially improves the representation of dust–gas interactions and their impacts on atmospheric composition and aerosol optics. Remaining uncertainties highlight the need for continued development of mineralogical datasets and chemical parameterizations, as well as the inclusion of anthropogenic dust sources and additional reactive gas species. Further progress would also benefit from high-resolution modeling of small-scale dust events and from expanded observational coverage over dust-affected urban regions.
  

DOCTORAL DEGREE IN GEOTECHNICAL ENGINEERING

• MORALES FLAUZÍN, GERARDO ABEL: Advances in geotechnical experimental techniques for unsaturated and liquefactable soils and tailings under different gravity accelerations (1g-Ng)
  
  Author: MORALES FLAUZÍN, GERARDO ABEL
  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: 16/02/2026
  Reading date: 27/03/2026
  Reading time: 15:00
  Reading place: ETSECCPB.UPC, Campus NordBuilding C1. Classroom: 002C/Jordi Girona, 1-308034 Barcelona
  Thesis director: PINYOL PUIGMARTI, NURIA MERCE | OLIVELLA PASTALLE, SEBASTIAN
  Thesis abstract: Flow liquefaction in contractive granular materials represents one of the most complex and hazardous failure mechanisms in geotechnical engineering, particularly in tailings dams. It is characterised by a sudden loss of shear strength under undrained conditions and by the rapid mobilisation of large volumes of material. Despite its practical relevance and the many documented failures, a detailed understanding of the internal processes governing the transition from sliding to flow remains limited, mainly due to the difficulty of obtaining experimental evidence during large deformations and post-failure stages.This doctoral thesis addresses the experimental study of post-failure behaviour, including strain localisation, retrogressive failure and flow liquefaction, through the development and application of non-invasive image-based measurement techniques. The main objective is to improve the observation, quantification and interpretation of deformation mechanisms and hydro-mechanical processes under large deformation conditions, and to apply these tools to the study of tailings failures.From a methodological perspective, the thesis refines the Eulerian–Lagrangian PIV-NP method. Its main limitations are systematically analysed, with particular emphasis on border-related errors, and specific correction strategies are proposed to enhance robustness and accuracy. These developments are validated using synthetic cases involving rigid motion and controlled deformation. In addition, PIV-NP is integrated with short-wave infrared (SWIR) imaging to allow non-intrusive measurement of surface moisture and degree of saturation in moving soils. This combined methodology, referred to as PIV-NP-Sr, is based on homographic transformations ensuring accurate spatial correspondence between visual and infrared images.The proposed techniques are first applied to a 1g test involving wetting-induced failure of a sand dam. Subsequently, a comprehensive experimental programme based on geotechnical centrifuge modelling is presented, conducted within the Geolab–SLIDAM project. This programme includes the characterisation of a mine tailing and seven small-scale centrifuge models tested under different acceleration levels, saturation conditions and failure activation mechanisms. Conventional instrumentation was deliberately minimised in favour of non-invasive imaging, allowing the full evolution of failure to be captured without disturbing material behaviour.The results enable detailed interpretation of shear band development, volumetric strain evolution and the progressive transition from sliding to flow-type behaviour. A novel observation is the occurrence of surface eruptions during post-failure stages, resembling miniature volcanoes. These events are shown to be preferentially located in zones of concentrated extensional strain. Based on the tracking of individual gas bubbles and their correlation with deformation fields, a preliminary interpretation involving gas entrapment, migration and pressurisation within the saturated granular medium is proposed.
  

DOCTORAL DEGREE IN MECHANICAL, FLUIDS AND AEROSPACE ENGINEERING

• KHOSRAVI, HAMID: Enhancing microfluidic and electrochemical sensors for biological and environmental analysis
  
  Author: KHOSRAVI, HAMID
  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: 27/02/2026
  Reading date: 13/04/2026
  Reading time: 15:30
  Reading place: Sala de conferències del TR5, ESEIAAT.
  Thesis director: CASALS TERRE, JASMINA
  Thesis abstract: The transition toward low-cost, portable, and environmentally conscious analytical technologies has intensified the pursuit of sustainable alternatives to conventional laboratory instrumentation. This thesis develops paper-based and electrochemical sensing platforms that prioritize circular-economy principles by employing renewable and waste-derived materials. Non-wood cellulose fibers were selected as substrates for microfluidic paper-based analytical devices (μPADs), while industrial mill scale was valorized to synthesize magnetite nanoparticles for electrode modification, demonstrating that sustainability and high analytical performance can be synergistic.In the first study, μPADs fabricated from alternative cellulose sources were evaluated. Their fiber morphology and porosity strongly influenced capillary flow and colorimetric responses. Compared to commercial cellulose papers, non-wood substrates enabled substantially faster wicking and significantly reduced detection time, underscoring their suitability for rapid, low-resource diagnostics.The second study focused on lactate detection using magnetite-modified electrodes. Wastederived Fe₃O₄ nanoparticles enhanced electron transfer and enzyme immobilization, enabling an exceptionally broad detection range alongside high sensitivity and a low detection limit. To the best of our knowledge, this work represents the first demonstration of a lactate biosensing platform that simultaneously achieves such a wide dynamic range while retaining high analytical sensitivity, making it suitable for applications from trace physiological monitoring to highly concentrated food and fermentation environments.Finally, a novel electrochemical strategy was developed for polyethylene terephthalate (PET) microplastic quantification in water. Leveraging the natural affinity between PET and magnetite nanoparticles, the approach transitions from the traditional use of magnetite for magnetic pre-concentration toward direct and quantitative electrochemical measurement, successfully validated in synthetic and real water matrices.Overall, this thesis demonstrates that renewable and waste-derived materials from non-wood cellulose to mill-scale-derived magnetite can serve as functional components in advanced sensing platforms, advancing sustainable analytical technologies for biomedical and environmental applications.
  
• SABÁN FOSCH, ALEJANDRO: End-to-end design and development of an Autonomous Flight Safety System enabling reusable space missions in Europe
  
  Author: SABÁN FOSCH, 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 MECHANICAL, FLUIDS AND AEROSPACE ENGINEERING
  Department: Department of Mechanical Engineering (EM)
  Mode: Normal
  Deposit date: 05/03/2026
  Reading date: 14/04/2026
  Reading time: 10:00
  Reading place: ESEIAAT - Aula 3.6
  Thesis director: SORIA GUERRERO, MANUEL | DIEZ LLEDO, EDUARD | SUREDA ANFRES, MIQUEL
  Thesis abstract: This dissertation presents the design, development, and validation of an Autonomous Flight Safety System (AFSS) tailored to the operational and regulatory needs of reusable launch vehicles in Europe. Motivated by the shift from expendable rockets to reusable systems and the consequent need for autonomous range safety, the research situates itself at the intersection of technology, safety assurance, and certification.A requirement-driven approach, grounded in European and international regulations, ensured alignment with certification pathways. A review of current FSS and regulatory frameworks established the baseline from which requirements were derived. These were structured through a model-based systems engineering methodology, implemented in ARCADIA and SysML, guiding functional decomposition and definition of a three-layered architecture. The AFSS design comprises four application modules: navigation, flight dynamics assessment, decision-making, and Integrated Vehicle Health Management (IVHM).Each module was independently implemented and validated. The navigation subsystem met outage-handling requirements, reliably bridging data gaps. The flight dynamics assessment integrated 3D flight corridor checks, and impact prediction with aerodynamic effects and dispersion evaluation at low operational cost. For reusable launchers, the IVHM subsystem is essential, as safe operation requires monitoring systems for re-entry. This module classified anomalies accurately, highlighting the trade-off between expert-tuned and data-driven approaches due to sensitivity to membership function parametrisation. The decision-making logic consistently executed termination rules under nominal and degraded conditions, confirming robustness.A RAMS (Reliability, Availability, Maintainability, and Safety) analysis critically assessed maturity. Navigation and decision-making were identified as the most safety-critical functions, with redundancy mitigating risks but leaving common-mode vulnerabilities. Prototype hardware (HW) was selected according to Technology Readiness Level (TRL) criteria, suitable for ground validation at TRL 7 system level. This reflected a focus on validating software and architecture, while dedicated space-qualified HW -required for certification under harsher conditions such as radiation and vibration- lay beyond scope.Integration testing guaranteed the correctness of the AFSS prototype before the ground campaign at the Kiruna spaceport to achieve TRL 7, a milestone in European AFSS development. The prototype demonstrated coherent behaviour across processors, reliable synchronization between redundant chains, and real-time telemetry from the Real-time target machine. Although processing loads neared the limits of the selected low-end HW, it met its main objective: validating the complete AFSS software chain. Nonetheless, borderline safety decisions under certain conditions showed that resilience depends on algorithmic choices, parametrisation, and execution margins.The research shows AFSS architectures are technically feasible, regulation-aware, and progressing towards operational use, though challenges remain. Future work should address processor scalability with multi-core, space-qualified platforms; enhance navigation robustness against GNSS jamming and spoofing; extend IVHM towards prognostics; and evaluate navigation architectures (IMU-only versus integrated IMU/GNSS) once launcher avionics are defined. Equally critical is institutional progress: certifying AFSS will require new regulatory frameworks and joint experimental programmes aligning technical validation with policy evolution.By combining regulatory awareness, rigorous engineering, and validation to TRL 7, this dissertation contributes not only a prototype but also a roadmap. It demonstrates feasibility while clarifying remaining challenges, providing a foundation for the safe deployment of autonomous flight safety in Europe's reusable launchers.
  

DOCTORAL DEGREE IN PHOTONICS

• PUJOL CLOSA, MARIA DEL PILAR: Wave Propagation in Hyperbolic Metamaterial Waveguides
  
  Author: PUJOL CLOSA, MARIA DEL PILAR
  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: 11/03/2026
  Reading date: pending
  Reading time: pending
  Reading place: pending
  Thesis director: ARTIGAS GARCIA, DAVID
  Thesis abstract: Low-loss waveguides are essential for energy-efficient photonic circuits, optical communications, and sensing applications. Over the past century, two lossless phenomena—Dyakonov modes and Bound States in the Continuum (BICs)—have been discovered in anisotropic waveguides, where permittivities differ but share the same sign. Hyperbolic metamaterials (HMMs) exhibit extreme anisotropy, with ordinary and extraordinary permittivities of opposite signs, enabling unconventional light manipulation. Their unique properties have attracted broad interest for applications including subdiffraction imaging, spontaneous emission control, and enhanced light-matter interactions. This raises a fundamental question: can extreme hyperbolic anisotropy support novel confinement mechanisms or new regimes of lossless propagation? Prior research on HMM waveguides has been constrained to simplified models or propagation along principal axes, leaving systematic exploration of arbitrary propagation directions, and the phenomena they may reveal, as a critical gap.To address this gap, this thesis develops a semi-analytical computational framework that combines a transfer-matrix formulation with a complex-plane Newton-Raphson root finder, enabling stable tracking of guided and leaky modes for arbitrary propagation directions. This tool allows systematic exploration of a wide range of parameters and configurations previously difficult to study.This thesis provides the most comprehensive investigation to date of light propagation in planar HMM waveguides. For the first time, the work analyzes both type I and type II HMM waveguides across all in-plane propagation directions and with arbitrary optic axis orientations. The analysis reveals how hyperbolic anisotropy fundamentally influences polarization, confinement, polarization exchange between modes, mode ordering, radiation mechanisms, and slow light arising from topological transitions. This establishes general trends, identifies new guiding regimes, and maps the landscape of wave phenomena in these extreme anisotropic systems.The exploration of leaky modes enabled a key discovery: Dirac points embedded in the Continuum (DECs), a novel class of topological degeneracy in non-Hermitian systems. DECs emerge when a symmetry-protected BIC and an interferometric BIC intersect linearly. At this intersection, the system exhibits a real eigenvalue, two orthogonal modes, and zero radiation loss—locally Hermitian behavior despite being embedded in a non-Hermitian system. The presence of both BICs suppresses Exceptional Points (EPs) and collapses the Fermi arc to a single point. Because DECs arise from universal BIC interactions rather than material-specific properties, this phenomenon extends beyond hyperbolic media, with implications in the fields of topological photonics and non-Hermitian physics.This thesis demonstrates the framework’s generality and reliability through application to anisotropic liquid-crystal waveguides, where predicted BIC trajectories match experimental observations, and to $\sigma$-near-zero metasurfaces, where the framework accurately reproduces published dispersion diagrams. These validations confirm its applicability beyond hyperbolic systems.This thesis establishes a comprehensive theoretical and computational understanding of wave propagation in planar HMM waveguides for both type I and type II configurations and discovers DECs as a novel physical phenomenon with implications beyond hyperbolic media. By revealing how extreme anisotropy enables new guiding regimes and loss suppression, this work advances the understanding of light confinement in open, strongly anisotropic systems and provides new routes for designing low-loss photonic devices.
  

DOCTORAL DEGREE IN POLYMERS AND BIOPOLYMERS

• COLOMBI, SAMUELE: Soft and Conductive Material Architectures for Flexible Electronics: from Hydrogels to Nanomembranes
  
  Author: COLOMBI, SAMUELE
  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: 20/02/2026
  Reading date: 14/04/2026
  Reading time: 10:30
  Reading place: EEBE - UPCSala Polivalent, Ed. Ahttps://eebe.upc.edu/ca/lescola/com-arribar
  Thesis director: ALEMAN LLANSO, CARLOS ENRIQUE | GARCÍA TORRES, JOSÉ MANUEL
  Thesis abstract: Soft materials are key enablers of the next generation of flexible and multifunctional systems for applications spanning biomedicine, energy, and environmental technologies. This thesis focuses on the design, fabrication, and implementation of (nano)engineered polymeric materials with tailored properties for use in soft electronic devices, drug delivery systems, solar-driven water evaporators, and functional scaffolds for tissue engineering. To this end, three main families of materials were developed: composite alginate (Alg)-based hydrogels, composite gelatin methacrylate (GelMA)-based hydrogels, and polylactic acid (PLA)-based nanomembranes. For each material platform, structure–property–function relationships were systematically investigated across distinct biotechnological scenarios to achieve enhanced performance. Alg-based composite hydrogels were engineered as versatile, water-rich platforms through the incorporation of functional nanomaterials (such as PLA nanofibers and gold nanoparticles), enzymes, and conducting polymers (e.g., PEDOT:PSS). These systems were designed to exhibit improved mechanical robustness, controlled porosity, and tunable physicochemical and functional properties while maintaining their intrinsic biocompatibility. By modulating composition, the Alg-based hydrogels were successfully applied as drug delivery matrices for the sustained release of lactate, as soft electronic platforms for temperature and H₂O₂ sensing, and as flexible energy-storage devices. In addition, the introduction of secondary covalent crosslinking was explored as a strategy to enhance operational stability in H₂O₂ biosensing and solar-driven steam generation applications. In parallel, bilayered PANI–PLA nanomembranes incorporating aligned gold nanopillars were fabricated as free-standing, lightweight, conformable, and mechanically stable platforms for skin electronics. These nanomembranes enabled simultaneous pH and non-enzymatic NADH sensing, demonstrating their suitability for monitoring skin physiology and infection-related biomarkers. Finally, GelMA hydrogels were nanoengineered with magnetoelectric and/or graphene-based nanomaterials to develop bioactive scaffolds for cardiac tissue regeneration. The incorporation of these nanomaterials allowed precise tuning of the mechanical, electrical, magnetic, and biological properties of the hydrogels. Their ability to support cell adhesion and proliferation, combined with their capacity to sense cellular activity, highlights their potential as multifunctional scaffolds for engineered tissues and bioelectronic interfaces. Overall, this thesis demonstrates that rational materials design across multiple length scales enables the development of soft matter systems with tailored and synergistic functionalities, paving the way toward next-generation flexible electronic devices, controlled therapeutic platforms, and sustainable water-treatment technologies.
  

DOCTORAL DEGREE IN SIGNAL THEORY AND COMMUNICATIONS

• GORT JELMER DIRK, BEREND: AI-Driven Zero-Touch Orchestration of Edge-Cloud Services
  
  Author: GORT JELMER DIRK, BEREND
  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: 10/03/2026
  Reading date: pending
  Reading time: pending
  Reading place: pending
  Thesis director: ANTONOPOULOS, ANGELOS | UMBERT JULIANA, ANNA
  Thesis abstract: 6G networks demand orchestration systems capable of managing thousands of distributed microservices under sub-millisecond latency constraints. Traditional centralized approaches introduce unacceptable delays, create single points of failure in heterogeneous edge-cloud infrastructures, and require constant attention from human operators. This dissertation addresses three critical challenges: (1) computational constraints that prevent the deployment of predictive models on edge devices, (2) lack of generalization of models across diverse types of applications, and (3) lack of validated autonomous orchestration without human intervention.To address these challenges, this dissertation develops three complementary frameworks that combine lightweight machine learning, attention-based deep learning, and agentic artificial intelligence for zero-touch service management in distributed 6G edge-cloud environments.The first contribution, AERO (Adaptive Edge-cloud Resource Orchestration), addresses the challenge of running predictions on resource-constrained edge devices. Current transformer models require millions of parameters (e.g., Pathformer: 2.4M), making them impractical for edge deployment. AERO achieves competitive accuracy with only 599 parameters, making edge deployment feasible and reducing reliance on cloud round-trips when local inference is preferred. Evaluations demonstrate sub-millisecond inference (0.38ms), 13% energy savings, and 99% fewer SLA violations compared to reactive scheduling, which allocates resources only after demand changes occur.The second contribution, OmniFORE (Framework for Optimization of Resource Forecasts in Edge-cloud networks), addresses the operational challenge of maintaining separate models per application. A single OmniFORE model generalizes across heterogeneous workloads without retraining, replacing the need for dedicated per-application models. Cross-dataset evaluation on industry-standard benchmarks (Google and Alibaba production traces) demonstrates 30% better accuracy than ModernTCN while maintaining 15× faster inference than AGCRN.The third contribution, AgentEdge, addresses the challenge of agentic orchestration in distributed edge-cloud environments. Existing agent frameworks target generic domains or centralized cloud infrastructures, leaving distributed 6G environments without autonomous management solutions. AgentEdge introduces multi-agent orchestration to this domain, translating natural language intent (e.g., "deploy with low latency") into validated orchestration actions across heterogeneous infrastructure. Evaluations demonstrate 78.3% success rate (2.76× higher than single-agent baselines), 10× reduction in API call variability, and power savings up to 300.8W across deployments scaling from 8 to 35 nodes.The research has produced 5 journal publications, 3 international conference papers in IEEE venues, and 1 Elsevier book chapter.
  
• KASULURU, VAISHNAVI: AI-Driven Network Service Management for Efficient and Sustainable Open-RAN Systems in 6G and Beyond
  
  Author: KASULURU, VAISHNAVI
  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: 24/02/2026
  Reading date: 15/05/2026
  Reading time: 14:00
  Reading place: ETSETB B3 Teleensenyament, Campus Nord UPC, Barcelona
  Thesis director: BLANCO BOTANA, LUIS | ZEYDAN, ENGIN
  Thesis abstract: This thesis presents an efficient and sustainable AI-driven resource management framework for next-generation Open Radio Access Network (O-RAN) in the context of the emerging 6G era. The framework operates in a cloud-native 6G environment and translates predictive intelligence into reliable, energy-aware orchestration. It combines advanced predictive modeling with optimization-based control to address challenges, like stochastic demand, multitenancy, and computational complexity in O-RAN. The predictive forecasting architecture is the core of the framework, quantifying uncertainty and interdependencies among network resources across multiple tenants. Probabilistic forecasting models generate distributions of future resource demands, enabling service providers to perform more informed and risk-aware resource orchestration in complex multitenant environments.Initially, the framework considered in this thesis considers univariate probabilistic estimators, including Simple-Feed-Forward (SFF), Deep Autoregressive Recurrent network (DeepAR), and Transformers, to predict individual resource demands and support effective provisioning in O-RAN. These models deliver efficient, agile, and uncertainty-aware resource predictions, which are integrated into a novel percentile-based orchestration strategy, Dynamic Percentile Adjustment Approach (DYNp). The proposed method dynamically adjusts the percentage to ensure efficient resource utilization in O-RAN systems. Selecting an appropriate percentile is critical for balancing resource waste and service reliability. However, univariate probabilistic estimates do not capture cross-resource interdependencies, leading to suboptimal decision-making. To address this limitation, the framework incorporates state-of-the-art multivariate probabilistic forecasting models such as Gaussian Process Vector Autoregression (GPVAR) and the Temporal Fusion Transformer (TFT). They jointly process multiple time series and provide robust estimates of future resource demands. These models effectively learn complex interdependencies among different resources and key parameter indicators across network slices and tenants. Furthermore, we have evaluated how low-rank approximation in GPVAR estimator enhances scalability and robustness by reducing the algorithm's training time. One of the main goals of this thesis is to achieve energy efficiency and effective resource management and sharing. By considering predictive intelligence together with power consumption, the proposed techniques proactively optimize the activation and deactivation of radio resources or radio units. This strategy significantly reduces power consumption while maintaining user experience and adhering to Service Level Agreement (SLA) guarantees. Furthermore, another relevant contribution of the thesis is the extension of the traditional cellular O-RAN architecture to include Cell-Free massive Multiple-Input and Multiple-Output (CF-mMIMO) networks, reflecting the architectural evolution for beyond 6G systems. This provides a scalable approach to ultra-dense, energy-efficient O-RAN deployments. Finally, the algorithm tools considered in the dissertation are implemented as modular applications to facilitate deployment across O-RAN. The cloud-native implementation of the forecasting and orchestration pipeline is a notable achievement. Each module has been containerized using Docker, and its functionality is exposed via Representational State Transfer (REST) APIs, such as Swagger. This enables the pipeline to operate as microservices, supporting flexible deployment, scalable execution and seamless integration within O-RAN. The thesis establishes a mathematical and architectural foundation for deploying AI-driven, sustainable, and energy-optimized O-RAN with uncertainty adaptation. It provides a basis for realizing intelligent, autonomous, and stable 6G networks and supports future research and industrial implementation of AI-powered O-RAN ecosystems.
  

DOCTORAL DEGREE IN STATISTICS AND OPERATIONS RESEARCH

• BORJA ROBALINO, RICARDO STALIN: Optimización bayesiana en técnicas machine Learning clásicas: redes neuronales y XGBoost y su aplicación como modelos predictores de diabetes en pacientes ecuatorianos
  
  Author: BORJA ROBALINO, RICARDO STALIN
  Thesis file: (contact the Doctoral School to confirm you have a valid doctoral degree and to get the link to the thesis)
  Programme: DOCTORAL DEGREE IN STATISTICS AND OPERATIONS RESEARCH
  Department: Department of Statistics and Operations Research (EIO)
  Mode: Normal
  Deposit date: 22/01/2026
  Reading date: 10/04/2026
  Reading time: 11:00
  Reading place: Dia 10 d’ abril de 2026 a les 11h del matí a la Sala de Juntes de la FIB
  Thesis director: MONLEON GETINO, ANTONIO | GIBERT OLIVERAS, CARINA
  Thesis abstract: Machine learning (ML) is a branch of artificial intelligence that allows human capabilities to be imitated through various algorithms and techniques that learn from data using learning processes (supervised, unsupervised, or reinforcement) for decision-making with minimal human intervention. Classic ML models have generated great results in the automation of classification and regression processes in various areas. Within classification, artificial neural networks (ANN) have gained relevance due to their ability to learn and model complex nonlinear relationships. Similarly, the XGBoost model based on decision trees has demonstrated great efficiency, speed, scalability, and performance, winning several competitions. On the other hand, Bayesian inference has provided a probabilistic and revolutionary framework for optimizing machine learning models, with the implementation of uncertainty in the estimation process, combining evidence with prior beliefs, in order to reduce overfitting and improve predictions by adjusting parameters and hyperparameters.This research aims to optimize two classic machine learning techniques (artificial neural networks and XGBoost) for classification using Bayesian inference and to build a diabetes prevention model for the Ecuadorian population. The study begins with a theoretical and mathematical conceptualization of each algorithm, followed by an analysis of the various points of intervention, programming, and implementation of Bayesian models using Markov chain Monte Carlo (MCMC) estimation techniques and variational inference (VI), validation using public databases, implementation of a client-server system with multiple specialized backends, and, finally, the development of a real application as predictors of type 1, type 2, and gestational diabetes.As a result, a Bayesian model was implemented in artificial neural networks (ANN) at two inference points. The first adjusted the parameters at each backpropagation step; however, it presented itself as an option with a prohibitive computational overhead. As a second intervention, an adjustment was made to the activation function in the final layer, obtaining positive and computationally viable results. In the case of XGBoost, the predictions were adjusted at each boosting step before vectorization, demonstrating high predictive power in both the use of the MCMC technique and IV. Validation with the Pima Indians Diabetes database and the use of various distribution functions demonstrated the robustness and sensitivity of the implemented models, while generalization and consistency were verified through application to various databases. In all cases, results superior to or equal to those obtained using the traditional model were obtained, depending on the characteristics of the data.In addition, a web application (client-server) was implemented with Bayesian proposals, allowing users to interact with the models in an easy and intuitive way, with options for data loading, parameter configuration and probability distributions, estimation techniques (MCMC or IV), training-validation process or use of cross-validation, real-time results, and model download options. The application of the Bayesian proposal to a real case, such as the prediction of type 1, type 2, and gestational diabetes, with data from Ecuadorian patients, presented encouraging results (accuracy = 99.47%), becoming the first predictive model for the three types of diabetes at the regional and national level, confirming that the use of this approach is an excellent alternative for the optimization of machine learning models.
  
• ROGNON, PAUL JORIS DENIS: Improving variable selection properties by leveraging external data.
  
  Author: ROGNON, PAUL JORIS DENIS
  Thesis file: (contact the Doctoral School to confirm you have a valid doctoral degree and to get the link to the thesis)
  Programme: DOCTORAL DEGREE IN STATISTICS AND OPERATIONS RESEARCH
  Department: Department of Statistics and Operations Research (EIO)
  Mode: Normal
  Deposit date: 09/03/2026
  Reading date: 20/04/2026
  Reading time: 14:00
  Reading place: Sala d'actes de la FMECampus Diagonal Sud, Edifici U. C. Pau Gargallo, 14 08028 Barcelona
  Thesis director: ROSSELL RIBERA, DAVID | ZWIERNIK, PÌOTR
  Thesis abstract: Sparse high-dimensional signal recovery is only possible under certain conditions on the number of parameters, sample size, signal strength, and underlying sparsity. I show that leveraging external information, as possible with data integration or transfer learning, allows pushing these mathematical limits. Specifically, I consider external information-dependent l0 penalties and Bayesian variable selection methods, show that they attain model selection consistency under milder conditions than standard methods, and that they also attain faster model recovery rates. First, I obtain results for oracle-based penalties and prior inclusion probabilities that have access to perfect sparsity and signal strength information. Those results provide an understanding of how and when external information helps variable selection. They also provide a theoretical benchmark to evaluate practical non-oracle selection methods using external information. Subsequently, I propose data-based procedures grounded in empirical Bayes methods that leverage external information to ease variable selection and do not require an oracle. I derive their properties in the particular case where external information partitions the set of variables in blocks with potentially distinct characteristics. Finally, I discuss a computational framework for the incorporation of external information in Bayesian variable selection through empirical Bayes in the general case.
  

DOCTORAL DEGREE IN STRUCTURAL ANALYSIS

• BORGES CAVALCANTI, DANILO: Finite Element Method with Embedded Strong Discontinuities for Coupled Hydro-Mechanical Problems
  
  Author: BORGES CAVALCANTI, DANILO
  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: Change of supervisor + Article-based thesis
  Deposit date: 09/03/2026
  Reading date: 20/04/2026
  Reading time: 14:00
  Reading place: Sala Zienkiewich (CIMNE) Building C1, UPC - Campus North Gran Capitan S/N 08034 Barcelona
  Thesis director: DE POUPLANA SARDÀ, IGNASI | CAMPOS RAMOS MARTHA, LUIZ FERNANDO | DE MESQUITA ROEHL, DEANE
  Thesis abstract: Coupled hydro–mechanical (HM) processes in fractured porous media govern the performance and safety of several subsurface engineering applications, where pressure-driven changes in stress and permeability can control injectivity, leakage pathways, and fault reactivation potential. This thesis develops a robust and versatile finite element formulation for transient HM problems in the presence of pre-existing strong discontinuities that remains practical for integration into standard finite element workflows. The proposed approach is formulated within the Embedded Finite Element Method (E-FEM) and grounded on the Strong Discontinuity Approach (SDA), enabling an implicit representation of fractures and faults while circumventing mesh conformity constraints. A unified description is introduced to model discontinuities acting either as preferential flow paths or as hydraulic barriers, capturing the longitudinal flow along the discontinuity and the transversal exchange with the porous matrix in steady-state and transient settings. The formulation is systematically verified against discrete fracture models with interface elements and applied to benchmark problems representative of fractured-reservoir conditions, including a coupled fault reactivation scenario. In addition, the thesis investigates the occurrence of spurious oscillations in cohesive traction fields along embedded discontinuities and demonstrates that the choice of an SDA-based embedded formulation can markedly improve traction smoothness. These improvements strengthen the use of E-FEM for HM assessments involving pre-existing fractures and faults.
  

DOCTORAL DEGREE IN THEORY AND HISTORY OF ARCHITECTURE

• DE SANTIAGO GARCIA, JAVIER NICOLAS: La imagen poética en los procesos de producción, proyectuales y habitar de la vivienda de autoproducción en Lomas del Centinela.
  
  Author: DE SANTIAGO GARCIA, JAVIER NICOLAS
  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: 09/02/2026
  Reading date: 08/04/2026
  Reading time: 16:00
  Reading place: ETSAB (Esc.Técn.Sup.Arquitect.Bcn)-Sala GradosAv. Diagonal, 649-BCNVideoconfer.: https://meet.google.com/qfv-wpcj-hnoInicio conexión: 15:30h local
  Thesis director: USANDIZAGA CALPARSORO, MIGUEL M. | SERRA PERMANYER, MARTA
  Thesis abstract: This thesis addresses the search for the poetic image in self-built housing in the Lomas del Centinela neighborhood, in Zapopan, Jalisco, Mexico. This search focuses on some of the processes identified with self-built housing, particularly in the production processes, design processes, and the dwelling process.The concept of poetic image is constructed through the discourses of authors such as Gaston Bachelard, Carlos González y Lobo, Steven Holl, Manuel Martín Hernández, Juhanni Pallasmaa, and Alberto Pérez-Gómez. It can be defined as the element that results from having imagined something with a specific purpose in mind. It is referred to as an element because it can manifest as a thought, a written text, a goal, a conceptual drawing, a method to achieve an end, a way of experiencing a place, of appropriating it, and of recognizing oneself in that journey.This study examines women from Lomas del Centinela who have self-built their homes, approaching their history and the forms of production they engaged in during the years they, along with their families, pursued the dream of building their own homes.During this research, they were interviewed, seeking in their discourses elements that could be considered poetic images, with the aim of exploring their self-building processes through the lens of the poetic image. Through this search, not only is the importance of imagination emphasized, but also the fact that self-building processes are not spontaneous. Rather, in much of their conception, construction, and inhabitation, they respond—beyond the management of minimal resources—to a strong imaginative component. This entails a profound reflection on the potential of self-built housing in terms of creative achievement.
  
• ROGER GONCE, JOAN: “El barrio que (nos) construimos” Creixement i desenvolupament urbà del barri de Roquetes de Barcelona, a través del Padró Municipal d’Habitants (1940-1978)
  
  Author: ROGER GONCE, JOAN
  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: 13/01/2026
  Reading date: 27/03/2026
  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: ROSSELLO NICOLAU, MARÍA ISABEL | OYON BAÑALES, JOSE LUIS
  Thesis abstract: This study addresses the urban history of the Roquetes neighborhood of Barcelona during the Franco period, with the aim of analyzing its formation, consolidation and transformation over the course of more than forty years of dictatorship. The work aims to provide data and a critical reflection on the social, economic and urban processes that shaped this working-class and markedly immigrant neighborhood, in a context of accelerated growth, precarious infrastructure and territorial inequalities.The meticulous analysis of the municipal population register, systematically cross-referenced with other demographic, labor and urban sources, has allowed us to delve deeper into key issues for understanding the neighborhood's trajectory: the migratory networks and chains that sustained its growth; the forms of work and the opportunities —or limits— of social mobility for its residents; the housing conditions and models of urban production; and, finally, the construction of the neighborhood as a space for coexistence, identity and sociability in a framework of institutional abandonment and neighborhood responses.Through this combination of perspectives and sources, the research provides an integrated look at Roquetes that contributes to the broader debate on urban peripheries, Franco's socialist regime and the everyday experiences of popular sectors in 20th-century Barcelona.
  
• ZIAIEBIGDELI, MOHAMMADAMIN: Computing quality in housing: examining the evolution and systematization in computational design in architectural plan design and analysis
  
  Author: ZIAIEBIGDELI, MOHAMMADAMIN
  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: 19/02/2026
  Reading date: 10/04/2026
  Reading time: 12:00
  Reading place: ETSAB (Esc. Técn. Sup. Arquitectura Bcn)-Sala GradosAv. Diagonal, 649-BCNVideoconfer.: https://meet.google.com/uoh-cdjb-mdpInicio conexión: 11:30h
  Thesis director: ROSSELLO NICOLAU, MARÍA ISABEL | HERNÁNDEZ FALAGÁN, DAVID
  Thesis abstract: This research undertakes a meticulous exploration into the systematization and analysis of architectural plans, tracing their evolution through the lens of computational design thinking from the 20th century to the present. The specific context is living spaces, where the quality and functionality are intrinsically tied to the systematic extraction and application of design parameters. This dissertation explores the evolution of computational thinking in architecture by bridging the theoretical paradigms introduced by pioneers Alexander Klein, Cedric Price, Christopher Alexander, and Nicholas Negroponte with contemporary design practices. Through an inductive methodology, three case studies are analyzed to demonstrate how computational tools and technologies enhance spatial quality, adaptability, and user engagement. Each case study corresponds to the key indicators established by these pioneers—spatial geometry, temporal adaptability, participatory design, and digital computation—illustrating their enduring influence on modern architecture. By mapping these foundational ideas to advanced practices such as parametric modeling, smart systems, and digital fabrication, this research highlights the practical relevance of these paradigms in addressing contemporary architectural challenges and shaping innovative, human-centered spaces.The journey begins with Alexander Klein’s Existenzminimum concept in the 1920s-1930s. His scientific approach to design emphasized space optimization and the systematization of plans using both qualitative and quantitative parameters, laying the groundwork for future computational design methods. As we move into the 1960s, we encounter Christopher Alexander’s ‘Pattern Language.’ Alexander brought a new perspective to plan systematization, employing rules and systems to extract design parameters and analyze architectural planning. His methodologies form a vital cornerstone of computational design as we know it today.In the 1970s, Cedric Price introduced a more dynamic approach. His vision of architecture, as exemplified in the 24 hour living toy project, accommodated changing user needs and behaviors. His focus on user-centric parameters marked a shift in computational design thinking towards adaptability and flexibility. Presently, computational design thinking in the context of living spaces aims to synthesize the lessons from these key figures, emphasizing the analysis and extraction of design parameters. These parameters, enriched by the advancements in technology and the increasingly user-centric approach, are utilized to optimize and improve the quality of living spaces This research’s trajectory, from past methodologies to contemporary practices, provides an in-depth understanding of how computational thinking and plan analysis have evolved. By extracting and studying the diverse parameters used across time, this research aims to enrich the computational design discourse and advance the design of living spaces.