DOCTORAL DEGREE IN AUTOMATIC CONTROL, ROBOTICS AND VISION

• DELMAS, GINGER: Linking Human Poses With Natural Language
  
  Author: DELMAS, GINGER
  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: 10/03/2025
  Reading date: 23/05/2025
  Reading time: 10:00
  Reading place: Sala d'Actes, Facultat de Matemàtiques i Estadística, Universitat Politècnica de Catalunya (FME), Carrer de Pau Gargallo, 14, 08028 Barcelona
  Thesis director: MORENO NOGUER, FRANCESC D'ASSIS | WEINZAEPFEL, PHILIPPE
  Thesis abstract: Human pose is key to multiple human-centric applications, in a wide range of domains such as art (person depiction), sport (fitness coaching), robotics (skill teaching), entertainment (motion capture in movies, digital animation) or digitization (avatar design). In order to materialize such systems, researchers have designed deep learning models which address the related, underlying tasks of pose-guided image synthesis, 3D human pose estimation, human motion generation, mesh synthesis, pose prior production, and so forth.Until very recently, human pose had mostly been studied in conjunction with images. The field twitched with the arrival of efficient language models, which fostered the incorporation of linguistic in vision frameworks, and thereby powered multi-modal applications.This thesis fits into this dynamic. We aim to leverage Natural Language (NL) to bud human pose understanding in human-centric tasks. In contrast to prior endeavors, we juggle with static 3D human poses, images and detailed NL texts all together. We further explore novel multi-modal applications, requiring fine-grained understanding of the human pose.First, to alleviate the lack of data, we introduce new datasets linking 3D human poses with NL texts. We notably investigate two settings. One where the text is a description of the target pose, and another where the text provides modification instructions to reach the target pose from a source pose. These datasets result both from (i) the collection of crowd-sourced annotations, and (ii) the automatic, rule-based generation of texts, which consists in the incorporation of classified pose measurements into templates sentences. Next, we use these datasets to develop several cross-modal generation models like text-driven pose synthesis, pose captioning, text-guided pose editing and generation of textual posture feedback. Eventually, we connect 3D, text and images through a novel combinating framework, so as to derive a versatile, multi-modal pose representation, to be leveraged for downstream tasks akin to pose estimation or NL posture feedback from visual input.In summary, we tackle multiple machine learning tasks entailing human pose understanding, thanks to the connection of human pose and Natural Language.
  
• TIAN, YI: Bio-inspired Event-driven Intelligence for Motion Estimation
  
  Author: TIAN, YI
  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: 18/03/2025
  Reading date: pending
  Reading time: pending
  Reading place: pending
  Thesis director: ANDRADE CETTO, JUAN
  Thesis abstract: Motion estimation problems can range from low degrees of freedom (DOF) ego-motion estimation to complex, high-DOF motion, which includes dense pixel displacement or optical flow. This information is essential for enabling robots to perceive and navigate their environments. However, existing vision systems for motion estimation are less robust and efficient than biological systems, largely due to limitations in sensor technology and processing methods. This thesis builds on the bio-inspired sensor -event camera-, and the brain-inspired computing approach -Spiking Neural Networks (SNNs)-, presenting a promising solution that bridges these gaps. Event-based cameras have high temporal resolution, low latency, reduced data redundancy, and are power efficient. These unique capabilities make them particularly well-suited for environments and tasks where traditional frame-based cameras struggle. They show great potential for the solution of motion estimation problems across a wide range of applications, such as providing accurate and low-latency motion estimation for autonomous vehicles or aerial robots. SNNs are inspired by how neurons in the human brain communicate through synapses using spikes, which are brief and discrete electrical signals that allow highly efficient and robust information processing. The thesis begins with estimating 3-DOF ego-motion, progresses to sparse optical flow, and ultimately tackles dense optical flow. In the first step, the thesis addresses event-based ego-motion estimation by integrating SNN approaches with traditional optimization-based techniques. It explores the ego-motion estimation problem from inference optical flow obtained by an SNN and proposes a pooling method to address the aperture problem encountered in the sparse and noisy normal flow output of the SNN. In the next step, modern artificial neural network (ANN) architectures are leveraged to improve event-based optical flow estimation. This step proposes a U-Net transformer-based architecture with a recurrent neural network as the backbone. In the final phase of this research, the visual transformer architecture is further extended to flow encoders, incorporating spatiotemporal attention to enhance the extraction of temporal information. This led to the development of a swin transformer-based ANN model and its spiking counterpart. Notably, this work marks the first use of spikeformers in event-based optical flow estimation, demonstrating the potential of combining transformer architectures with SNNs for regression tasks. Overall, this thesis advances the understanding of motion estimation using event cameras. It sets the stage for their application in real-world scenarios such as high-speed object tracking and simultaneous localization and mapping (SLAM). The biologically inspired methods developed in this thesis offer promising avenues for balancing the performance and efficiency of computer vision and robotics systems, paving the way for future innovations in this field.
  

DOCTORAL DEGREE IN BIOMEDICAL ENGINEERING

• DAVIS ORTIZ, ALBERTO: Development of a Fuzzy Logic-Based Algorithm for Stroke Detection in Non-Contrast Computed Tomography Images
  
  Author: DAVIS ORTIZ, 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 BIOMEDICAL ENGINEERING
  Department: Department of Automatic Control (ESAII)
  Mode: Normal
  Deposit date: 18/02/2025
  Reading date: 09/05/2025
  Reading time: 16:30
  Reading place: Aula 28.8, 1a planta, Edifici I, Escola Tècnica Superior d'Enginyeria Industrial de Barcelona, Campus Diagonal Sud, Av. Diagonal, 64708028 Barcelona
  Thesis director: AYMERICH MARTINEZ, FRANCISCO JAVIER | GORDILLO CASTILLO, NELLY
  Thesis abstract: The present work addresses the problem of early stroke detection, not only from the perspective of detection accuracy, but also focusing on computational efficiency, considering the limited availability of cases for training. To this end, several algorithms have been developed to optimize different processes, such as a brain extraction algorithm, an affine transform algorithm, and a specific adaptive filter for noise in computed tomography images. This research has generated valuable resources, such as a brain atlas of healthy Mexican patients and a template of vascular territories. The use of atlases allowed the extraction of features from specific areas. The features used were relatively simple, such as histograms and Haralick textures, which were combined with linear discriminant analysis and an adaptive neuro-fuzzy inference system as a second layer of feature extraction, and finally with a support vector machine as a classifier. Together, these methods achieved a performance of 98.25%. The results show that using the adaptive neuro-fuzzy inference system as a feature extractor improves the performance of other classifiers due to its ability to handle uncertainty and identify nonlinear relationships between variables. This study contributes to the development of low computational cost algorithms and provides new perspectives and tools that could be applied in a real environment in the future
  

DOCTORAL DEGREE IN CHEMICAL PROCESS ENGINEERING

• AGRELO LESTÓN, ASIER: Development of metal-enhanced TiO2-based photocatalysts for hydrogen production
  
  Author: AGRELO LESTÓN, ASIER
  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: 02/04/2025
  Reading date: 02/06/2025
  Reading time: 11:00
  Reading place: Campus Diagonal Besòs, Edifici I (EEBE)Sala Polivalent, Edifici I - I.01Av. Eduard Maristany, 16 08019 Barcelonahttps://eebe.upc.edu/ca/lescola/com
  Thesis director: LLORCA PIQUE, JORDI | SOLER TURU, LLUIS
  Thesis abstract: Human activity has led to rising greenhouse gas levels, altering climate patterns and intensifying weather events. Therefore, a transition to a decarbonized energy system is needed, with hydrogen as a promising energy vector alongside solar and wind energy. However, current hydrogen production methods, such as steam methane reforming, generate significant CO2 emissions. Sunlight-driven water splitting offers a sustainable alternative, though efficiency improvements are required for industrial viability.This PhD thesis focuses on developing novel TiO₂-based catalysts for photocatalytic hydrogen production.Chapter 3 was conducted with the SYMAC team from Université Toulouse 3-Paul Sabatier. A TiO₂ catalyst was decorated with Cu nanoparticles stabilized by quinidine and compared to a sample prepared via incipient wetness impregnation (IWI) using L-ascorbic acid. The quinidine-stabilized sample exhibited 5 times superior activity under UV, as well as activity enhancement under Uv-visible irradiation. UV-vis spectroscopy revealed a plasmonic band relative to Cu, and a decrease in the bandgap was confirmed by Tauc plots. XRD confirmed Cu deposition and predominant anatase phase of the TiO2. TEM confirmed presence of Cu nanoparticles that XAS and XPS identified predominant metallic nature with minor oxide contributions.Chapter 4 was carried out with the Supra- and Nanostructured Systems group at Universitat de Barcelona (UB). Hybrid TiO₂ photocatalysts were prepared with Au(I) complexes and Au(0) systems were developed as co-catalysts. Three catalyst series incorporating coumarin-based ligands were evaluated under light and heat. Two (1 wt.% co-catalyst) were prepared via IWI and ball milling (BM), while a third (0.25 wt.% Au) was synthesized by IWI. IWI-prepared samples showed superior activity, achieving up to 2.7 times the H₂ production of conventional Au/TiO₂. UV-vis spectroscopy confirmed plasmonic bands relatives to Au and Tauc plots revealed bandgap narrowing. TEM, HAADF-STEM, and XPS confirmed the presence of Au nanoparticles with a predominant metallic nature.Chapter 5 focused on Pt/TiO₂ photocatalysts synthesized by BM, optimized through a design of experiments (DoE) approach. The most active sample was 1.4 times more efficient than an IWI-prepared Pt/TiO2 reference under UV light irradiation. HAADF-STEM-EDX revealed Pt atoms dispersed on TiO₂, with post-reaction growth into nanoparticles while there was presence of some Pt atoms dispersed. XPS confirmed partial Pt reduction during the reaction.Chapter 6 explored bimetallic PdCu photocatalysts with a total metal loading of 1 wt.%. A Pd:Cu atomic ratio of 1:2 was chosen after a screening from 3:1 to 1:3. The bimetallic sample outperformed theoretical activity of the combination of thus metals under UV light by 27%, and Cu incorporation enhanced H₂ production under UV-vis irradiation. BM-prepared samples were 1.2 times more active than IWI ones. Pd stability was improved with Cu incorporation, as seen in long-term tests, with less activity loss compared to monometallic Pd. Raman spectroscopy indicated strong metal-support interactions. UV-vis spectroscopy and Tauc plots showed enhanced visible absorption and bandgap narrowing, respectively. HAADF-STEM-EDX revealed bimetallic PdCu nanoparticles in BM samples, whereas IWI samples had separate Pd and Cu nanoparticles. BM also constrained Pd growth, as Pd nanoparticles in the monometallic sample grew 3.5 times during the reaction. XPS showed Pd reduction in both samples, with complete reduction in BM-prepared catalysts, further supported by H₂-TPR.
  

DOCTORAL DEGREE IN CIVIL ENGINEERING

• , DUOLAN: Integration of Spatial and Temporal Patterns for ecological environment management in River-Riparian System
  
  Author: , DUOLAN
  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: Article-based thesis
  Deposit date: 18/03/2025
  Reading date: pending
  Reading time: pending
  Reading place: pending
  Thesis director: BLADE CASTELLET, ERNEST | SANCHEZ JUNY, MARTI
  Thesis abstract: Rivers are important carriers of water resources and important components of ecosystems. In some areas, rivers have been artificially narrowed, riparian areas have been encroached upon, riparian resources have been over-exploited, and artificial restrictions have been placed on the river channel. This resulted in the loss of the river's role in receiving and storing flood waters, which leading to the collapse of river banks and the destruction of river embankments, severely affecting the stability of the river, threatening the safety of bridges, culverts and other critical river-related infrastructure, and endangering the ecological environment. The definition of riparian zones is particularly important for the management and protection of rivers. In the implementation of policies to promote river management in various countries, emphasis has been placed on strengthening the management of riparian zones, ensuring the safety of flood control and giving full play to the comprehensive ecological benefits of rivers. In recent years, various countries have proposed laws and regulations in recent years mainly to control overdevelopment, restore natural vegetation growth in riparian areas, protect habitats and achieve flood control. With the progress of water-related social development, the balance between environmental impact and benefits is increasingly emphasized. Changes in river shape, man-made riverbeds, and riverbank construction affect aquatic life and destroy wildlife habitats. River regulation also alters ecosystems. To reduce these impacts, government agencies implement protocols for riparian assessment and monitoring, including physical habitat, hydromorphological, and hydrological regime evaluations.The research first begins with a retrospective analysis as the starting point to acquire how existing laws and regulations on development and restoration lack effective integration and induce weak adaptability. A river-riparian model is developed based on two-dimensional hydraulic modelling integrated with numerical modelling by relying on topographical, hydrological, vegetation, and soil data to analyze the hydro-ecological cycle within the riparian zone and delineate the boundary of riparian. The model aims to provide a site-specific approach to riparian zone delineation. In addition, a system of parameters for ecological status assessment is proposed which focuses on the main contradictions between the environment conservation and the ecosystem services of riparian zones. In order to develop and analyze strategies for a good ecological status of the water bodies and riparian zones, the methodology of riparian zone delineation will provide tools for enhancing the coordination of the needs of riparian resource development and ecosystem protection and use, and the ecological environment assessment system will evaluate the hydromorphological quality and promote the healthy development of the ecological environment. The findings of this research propose a convenient and effective method for delineating riparian zones which can be basically universally applied. It is noteworthy for its applicability in riparian zone management practices and as a reference for policy strategy development. The proposed quantitative evaluation method covers the key aspects of hydromorphological quality evaluation. This eliminates the highly subjective assignment of weights and classification of evaluation levels while also avoiding the inclusion of complex calculation procedures. The river-riparian areas evaluation method allows the decision-makers to easily analyze the problems through the resulting calculations and lay the foundation for further solutions.
  

DOCTORAL DEGREE IN COMPUTER ARCHITECTURE

• PUJOL TORRAMORELL, ROGER: Improving Real-Time Guarantees of Cache Coherence and Advanced Interconnections in Real-Time Systems
  
  Author: PUJOL TORRAMORELL, ROGER
  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: 18/03/2025
  Reading date: pending
  Reading time: pending
  Reading place: pending
  Thesis director: CAZORLA ALMEIDA, FRANCISCO JAVIER | ABELLA FERRER, JAIME
  Thesis abstract: The dissertation, research on enhancing timing predictability and performance for Critical Real-Time Embedded Systems (CRTES), focusing on Multi-Processor Systems on Chip (MPSoCs). CRTES are essential in critical domains like automotive and avionics, where complex functionalities and high performance are increasingly required for operations such as AI and multi-sensor data processing. However, MPSoCs face significant timing verification and validation (V&V) challenges, especially related to shared resources like caches and interconnects, which can introduce unpredictable delays. This thesis addresses three core areas to improve CRTES predictability: cache coherence, interconnection predictability, and application performance through vector extensions.Cache Coherence: In MPSoCs, cache coherence protocols ensure consistent data across multiple cores, but shared caches introduce contention that affects timing predictability. Traditional approaches to improving coherence often involve modifying protocols, a costly and complex task. This thesis takes an alternative approach by leveraging hardware event monitors (HEMs) to observe cache contention, providing valuable data for timing V&V without altering existing protocols. This methodology is applied to commercial MPSoCs like the NXP T1040 and T2080, which are widely used in real-time domains.On another note, the Remote Protocol-Contention Tracking (RPCT) method is proposed, which enables fine-grained tracking of delays due to inter-core contention, offering insights into cache coherence impacts on software predictability and informing developers on optimization strategies. Additionally, the thesis proposes a novel Multiple HEM Validation (MHV) method to improve the accuracy of contention measurements by validating HEM reliability through inter-HEM relationships, mitigating known issues with single-event HEM inaccuracies.Interconnections: MPSoCs rely on point-to-point (P2P) communication protocols like AXI4 for data transfer between cores, but the standard AXI protocol lacks timing constraints, making it unpredictable under real-time requirements. Addressing this, this thesis introduces AXI4 Real-Time (AXI4RT), an extension to the AXI protocol that specifies timing parameters to control the duration of transactions between manager and subordinate interfaces. By defining timing guarantees directly within the protocol, AXI4RT ensures predictable communication, enhancing system reliability for real-time applications. Additionally, this thesis provides some initial steps for contention tracking on modern AXI5 interconnects by doing an in-depth analysis how can contention be tracked with currently available HEMs and proposing some HEMs that could improve this tracking.Application Performance with Vector Extensions: To meet growing performance demands in CRTES, MPSoCs often use GPUs and custom accelerators, but these present certification challenges due to their complexity and unpredictable timing. This thesis explores using vector extensions (VExt) as an alternative. Single Instruction Multiple Data (SIMD) processing units are already available in many embedded processors, which perform parallel operations on multiple data elements, effectively improving data processing speeds. Unlike GPUs, VExt are integrated within processors and comply with high-integrity system standards, making them easier to certify. The thesis provides an analysis of VExt in COTS processors like NVIDIA’s AGX Xavier and show their potential to enhance performance while maintaining compliance with standards such as MISRA-C.In summary, this thesis advances the state-of-the-art in CRTES predictability, presenting solutions that ensure more reliable timing for complex embedded systems in safety-critical applications. By addressing cache coherence, interconnect timing, and performance, this thesis provides tools and methodologies for better timing analysis, enabling MPSoCs to improve real-time guarantees.
  
• SEYGHALY, RASOOL: A Federated Learning Approach to Smart Advertising
  
  Author: SEYGHALY, RASOOL
  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: 17/03/2025
  Reading date: pending
  Reading time: pending
  Reading place: pending
  Thesis director: GARCÍA ALMIÑANA, JORDI | MASIP BRUIN, XAVIER
  Thesis abstract: This thesis presents a Federated Learning-based Smart Advertising System designed to enhance user engagement, optimize network efficiency, and ensure data privacy in digital advertising. Traditional advertising systems face significant challenges in balancing personalization with privacy, managing network overhead, and scaling efficiently. This study addresses these issues by integrating Edge Computing and Federated Learning (FL) to enable real-time, decentralized ad targeting while keeping user data secure.The proposed system consists of a decentralized recommendation engine, where local models are trained on users’ devices and aggregated using meta-heuristic optimization techniques, particularly the Whale Optimization Algorithm (WOA). Experimental results demonstrate that WOA outperforms other aggregation techniques, such as the Firefly Algorithm (FA) and Bat Algorithm (BA), in terms of convergence speed and overall efficiency. The system also leverages formal verification techniques, including model checking, to ensure correctness, security, and compliance with privacy regulations.Comprehensive evaluation through both simulated and real-world case studies (such as the AROUND system) shows that the proposed architecture reduces network traffic, minimizes computational overhead, and significantly improves Click-Through Rates (CTR) and user engagement compared to traditional centralized models. The system is particularly beneficial for applications in museums, shopping malls, and retail chains, providing real-time tracking, indoor mapping, and personalized content delivery.The findings underscore the potential of Federated Learning and Edge Computing in privacy-preserving smart advertising, offering a scalable, cost-efficient, and user-centric solution for the future of digital marketing.
  

DOCTORAL DEGREE IN CONSTRUCTION ENGINEERING

• RAMONELL CAZADOR, CARLOS: Graph-driven digital twins as assistants to bridge maintenance
  
  Author: RAMONELL CAZADOR, 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 CONSTRUCTION ENGINEERING
  Department: Department of Civil and Environmental Engineering (DECA)
  Mode: Normal
  Deposit date: 10/03/2025
  Reading date: 28/05/2025
  Reading time: 10:00
  Reading place: C1-002
  Thesis director: CHACÓN FLORES, ROLANDO ANTONIO
  Thesis abstract: Bridges are vital components of transport infrastructure networks which are facing a widespread lack of resilience due to aging and changing environmental conditions. Finding more efficient methods for monitoring bridge networks and effectively planning their maintenance is crucial for maintaining reasonable serviceability levels. Simultaneously, digital twins are emerging across industries as dynamic digital replicas of physical assets. These are continuously updated with information from their physical counterparts and serve as the foundation for digital tools that enhance workflows in decision-making processes throughout the lifecycle of any product.This dissertation translates the concept of digital twins to the bridge maintenance domain and presents a framework for developing graph-driven digital twin systems to assist bridge managers in tracking the state of their asset portfolio.For this purpose, two different proof-of-concept systems are presented: System A and System B. Both systems are cloud-based, modular, and use graphs to integrate multiple data sources describing the bridges, their context, and relevant maintenance information. The systems are tested with real data corresponding to two demonstration cases of road and railway bridges in the Spanish infrastructure network. Through these demonstrators, the digital twin systems are developed to integrate BIM, GIS, sensor time-series data, and data related to the results of monitoring processes that is structured according to regional standards.System A focuses on hosting digital twins of individual bridges. It uses a labelled property graph (LPG) to interconnect IFC data with IoT sensor data and the results from visual inspections and load tests. Data integration is achieved by matching GUIDs of data contained the graph with data stored in the different databases and systems connected. The implementation of the system is demonstrated through a web-based digital twin platform, containing applications that allow visualizing and interacting with contextualized inspection and load test data.System B focuses on interconnecting multiple bridge digital twins within the same network. It employs a knowledge graph built from Resource Description Framework (RDF)-based graphs and a set of ontologies. The system integrates geographical data according to INSPIRE data models, IFC models, and data from visual inspections. The system presents a data management approach based on strata, which manage and compartmentalize information subsets, and implements the information containers for linked document delivery (ICDD) standard for exchanging graph data with linked documents. The system is demonstrated through a set of fictitious scenarios that simulate interactions between bridge administrators and third parties.Through these systems, this dissertation demonstrates the usefulness of graph technologies in developing digital twins of bridges that are aligned with current industry standards and practices. It emphasizes the advantages of the Knowledge Graph-based approach for simplifying interactions with connected applications, enabling decoupled application development, and enhancing the system scalability and expandability with new datasets. Notwithstanding, real implementation of these systems requires further validation and use cases, as well as collaboration among system developers, administrators, academia, and industry stakeholders to generate a coherent digital ecosystem that enhances the efficiency and productivity of bridge maintenance practices.
  

DOCTORAL DEGREE IN EARTHQUAKE ENGINEERING AND STRUCTURAL DYNAMICS

• RODRÍGUEZ SÁNCHEZ, JULIO: Nonlinear Identification of Underground Seismic Ground Motions From Surface Records
  
  Author: RODRÍGUEZ SÁNCHEZ, JULIO
  Thesis file: (contact the Doctoral School to confirm you have a valid doctoral degree and to get the link to the thesis)
  Programme: DOCTORAL DEGREE IN EARTHQUAKE ENGINEERING AND STRUCTURAL DYNAMICS
  Department: Department of Civil and Environmental Engineering (DECA)
  Mode: Normal
  Deposit date: 19/12/2024
  Reading date: pending
  Reading time: pending
  Reading place: pending
  Thesis director: LOPEZ ALMANSA, FRANCISCO | LEDESMA VILLALBA, ALBERTO
  Committee:
       PRESIDENT NO PRESENCIAL: BENAVENT CLIMENT, AMADEO
       SECRETARI: VARGAS ALZATE, YEUDY FELIPE
       VOCAL NO PRESENCIAL: PINZÓN, LUIS ALEJANDRO
  Thesis abstract: In earthquake engineering, generally only surface records are available; therefore, the motion of the lower soil layers must be estimated by depropagation analysis. Underground accelerograms are relevant in earthquake-resistant design of structures with buried parts, in irregular terrain, in earthquake-triggered landslides, and in soil-structure interaction, among other situations. These considerations highlight the relevance of the problem analyzed; regarding its mathematical formulation, if the soil behavior is nonlinear, it is far from trivial.The common practice in Earthquake Engineering consists of using a deconvolution process for obtention of ground motion at the base of the numerical model used for seismic analysis of underground structures. The drawback of this method is that, as it is carried out in the frequency domain, it cannot simulate the variation of the nonlinear characteristics of the soil during seismic excitation, but it hypothesizes that the mechanical properties of the soil are invariant for its whole duration. This leads to inaccurate calculation of excitation at lower soil layers that are especially acute when the soil column is weak or earthquakes are strong.This thesis presents an algorithm to accurately estimate, from surface records, the motion of the lower soil layers considering nonlinearity in soil nonlinear behavior by a modified Masing model. The proposed algorithm is 1D and the soil domain to be analyzed is discretized in layers; the ensuing equations of motion are solved in discrete time using the Newmark method. Given that this problem is numerically ill-conditioned due to the singularity of the mass matrix, a nonlinear Bayesian Kalman Filter-type method is used to estimate the solution.Soil propagation software is developed in Python programming language, incorporating state-of-the-art considerations about numerical simulation of soil behavior under seismic loading. This program is tested against closed-form solution for vibration of soil columns and site response analysis conducted using the widely used DEEPSOIL program to check its accuracy in computation of soil profile behavior under seismic conditions with satisfactory results.Then, the soil propagation software is coupled with the Unscented Kalman Filter algorithm to identify the input excitation at bedrock given the acceleration record at site surface. Several variations of this Bayesian filter are explored. Results of identification from both closed-form solutions for vibration of soil columns and site response analysis carried out with DEEPSOIL suggest that the proposed back-analysis algorithm for the obtention of acceleration time series at bedrock given surficial measurements is accurate, especially when compared to the deconvolution procedure.Finally, a sample underground structure modeled in PLAXIS2D is subjected to two ground motions at base: one is a deconvolved motion and the other is a depropagated accelerogram obtained through the identification process developed in this research. Difference in resulting structural forces from both records highlights the importance of adopting nonlinear algorithms for determination of input excitation at base for an adequate and safe design of underground structures.
  

DOCTORAL DEGREE IN ELECTRONIC ENGINEERING

• MORADMAND JAZI, HAMED: Design and implementation of lowinterference, high efficiency, power electronicbased power system for PV applications
  
  Author: MORADMAND JAZI, HAMED
  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: 13/03/2025
  Reading date: pending
  Reading time: pending
  Reading place: pending
  Thesis director: MARTINEZ GARCIA, HERMINIO | VELASCO QUESADA, GUILLERMO
  Thesis abstract: Nowadays, high step-up converters with fast-dynamic response are demanded for many applications such as uninterruptible power supplies which are used to feed sensitive loads and DC-DC converters in grid connected micro inverters to absorb the maximum power from the PV panels. Several studies have been carried out on high step-up converters to increase voltage gain and efficiency as well as reduce the voltage stress of semiconductors while less attention has been paid to their dynamic response. A converter which can compensate load variations rapidly would have faster dynamic response and lower undershoot and overshoot output voltage and current. In this research, various switching converters will be investigated to achieve new topologies having the capability of faster dynamic response and obtaining higher voltage gain for the above-mentioned applications. Merging some converters has the potential of removing right half plane zero and making converters respond load variations at a faster pace without making any changes in the control circuit and filters. If the integrated converter can deliver power form the input to the load in all operating modes whether the switch is on or not, the converter would compensate load variations with lower interruption. This theory can be evaluated and proved by doing some theoretical and mathematical calculations on the control response and the situation of Zeros and Poles of the closed loop transfer function of the converter. To rate the achievements of this research, the dynamic quantities in the step response of the converters (e. g. overshoot, rise time, and settling time) can be investigated. Also, the voltage gain and efficiency of the converters are important qualities which have to be considered in comparisons. A time table is considered for each stage to ensure that this research can be finished through the next three years.
  

DOCTORAL DEGREE IN MARINE SCIENCES

• RAYA RODRIGÁLVAREZ, VANESA MARIA: Spatial and temporal dynamics of larval fish communities in relation to environmental variability in the NW Mediterranean
  
  Author: RAYA RODRIGÁLVAREZ, VANESA MARIA
  Thesis file: (contact the Doctoral School to confirm you have a valid doctoral degree and to get the link to the thesis)
  Programme: DOCTORAL DEGREE IN MARINE SCIENCES
  Department: Department of Civil and Environmental Engineering (DECA)
  Mode: Normal
  Deposit date: 10/03/2025
  Reading date: 15/05/2025
  Reading time: 10:00
  Reading place: ETSECCPBUPC, Campus NordBuilding C1. Room 002.C/Jordi Girona, 1-308034 Barcelona
  Thesis director: SABATÉS FREIJÓ, ANA MARIA
  Thesis abstract: The early developmental stages of fish, eggs and larvae, found in the planktonic environment are subject to a high mortality. Thus, the study of larval survival has been a key issue in fisheries science since the early 20th century. Spatial patterns in the larval fish communities are influenced by a complex array of environmental processes that interacts with fish biology at different temporal and spatial scales. These processes include those of large scale, such as climate patterns and seasonal and interannual environmental variability, which determine adults’ distribution and their spawning strategies. At local and short time scale, larval fish communities are shaped by the hydrodynamics that influence fish larval dispersal and retention, and by biologic factors, such as food concentration and predation, that ultimately determine their survival.This thesis characterises the structure of the larval fish community in summer and winter in the Catalan coast (NW Mediterranean), an area with a wide array of environmental conditions and high hydrodynamic activity. The aim is to understand its spatial and interannual variability in response to changes in environmental conditions, including oceanographic variables and hydrodynamic processes. Within the context of climate change, the thesis describes long-term changes in the structure of the summer larval fish communities and aims to understand the interactions between larvae of established species and species that are expanding northwards in the area.To investigate the influence of winter environmental conditions on the structure of fish larval communities, two winters, 2017 and 2018, with contrasting environmental conditions were compared. 2017 was mild, while 2018 was more severe, with intense vertical mixing and deep-water formation and cascading events that enhanced shelf-slope water exchanges. Differences in the structure of larval fish community were found in relation to shelf-slope water exchange processes.A high spatial heterogeneity in larval fish communities was detected in the summers of 2003, 2004 and 2012, related to environmental factors, such as the continental shelf structure, latitudinal difference in surface temperature, primary productivity, and stratification level. Hydrodynamic structures such as instabilities of the Northern Current and anticyclonic eddies, also played an important role in the configuration of these communities.In summer, over three decades, 1980s, 2000s and 2010s, an increase in sea water temperature and a decrease in chlorophyll were detected. Changes in the composition and abundance of the larval fish community were also detected. These were mainly due to the presence of warm water species in the area for the first time, or to their increase in abundance, in the 2000s in relation to the northward expansion of the adults' range. Other species showed a decline in abundance over time, probably due to the decrease in chlorophyll.This work quantitatively compared the survival chances for larvae of E. encrasicolus (a established species) and S. aurita (a species expanding northwards). To this aim, a new method, the Box-Balance Model, was developed to evaluate the role of hydrodynamic structures in their mortality. The model revealed that despite the warming trend would contribute to the expansion of S. aurita, it has not yet developed an adaptation strategy as successful as that of E. encrasicolus, a well-established species in the area.
  

DOCTORAL DEGREE IN MECHANICAL, FLUIDS AND AEROSPACE ENGINEERING

• MORENO MARTÍN, SIRO: Collocation methods for the synthesis of graceful robot motions
  
  Author: MORENO MARTÍN, SIRO
  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: 10/03/2025
  Reading date: pending
  Reading time: pending
  Reading place: pending
  Thesis director: CELAYA LLOVER, ENRIC | ROS GIRALT, LLUIS
  Thesis abstract: Graceful motion can be loosely defined as the one we observe in natural movements executed by animals and humans, which are characterized by being agile, efficient, and fluid. The generation of graceful robot motions is typically sought through the minimization of cost functions involving not only path length, but also aspects related to smoothness, like the time derivative of acceleration, called jerk, or that of the controls. A widely used approach to compute optimal trajectories is through direct collocation, a technique that converts the continuous-time optimal control problem into a finite-dimensional NLP problem. Collocation proceeds by discretizing the trajectory using so-called collocation points, and imposing the dynamics constraints at such points. The formulation of most collocation methods, however, assumes that the system is governed by a first order ODE, whereas robotic systems are typically described by second or higher order ODEs. As a result, the usual practice is to initially convert those ODEs into first order form via introducing new variables, and adding new equations that link these variables with their integral counterparts. An often overlooked effect of this transformation is that it generates inconsistencies between the trajectory of each variable and that of its time derivative. This is so because a collocation method only imposes the differential relationships at the collocation points, but not in between such points. A closer examination of this effect reveals that the dynamic equations, which should be satisfied in the collocation points, are actually violated in them, despite apparently having been enforced. This thesis introduces new collocation methods designed to overcome these problems. Specifically, we develop improved versions of the most popular piecewise and pseudospectral collocation schemes, including the trapezoidal and Hermite-Simpson methods, as well as the LG, LGR, and LGL methods. The new algorithms are able to treat differential equations of arbitrary order M ≥ 1 without having to convert them into first order form. In all of them, the trajectory obtained for each variable coincides exactly with the time derivative of its corresponding integral variable, and the dynamic constraints are satisfied accurately at the collocation points. These properties allow a drastic reduction of the dynamics error of the obtained trajectories in many cases, meaning that the governing equations are better respected along the continuous time horizon of the problem. Our methods also provide trajectories that are smoother than those of conventional ones, and easily treat variables such as jerk or the time derivative of the controls in the cost function. An hp adaptive refinement algorithm is also proposed to combine the benefits of our piecewise and pseudospectral methods so as to speed up convergence to the solutions.
  

DOCTORAL DEGREE IN POLYMERS AND BIOPOLYMERS

• DOROST, POROCHISTA: Nanoparticles made of poly(gamma-glutamic acid) derivatives for drug delivery systems
  
  Author: DOROST, POROCHISTA
  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: 14/03/2025
  Reading date: pending
  Reading time: pending
  Reading place: pending
  Thesis director: GARCIA ALVAREZ, MONTSERRAT
  Thesis abstract: Polymers have become one of the primary options in biomedical fields due to their diverse properties, functionalities, and applications. Characteristics such as mechanical strength, biocompatibility, and biodegradability have made these materials highly attractive for various medical applications. One of the most intriguing applications of these polymers is drug delivery. Biodegradable polymers and copolymers are the primary materials used for producing temporary medical and pharmaceutical devices. These polymers can be chemically synthesized or naturally produced.Biotechnological polymers, produced through biotechnological processes, have garnered significant attention due to two major advantages. First, they are derived from renewable resources; second, as they are biologically produced, they are usually biocompatible, biodegradable and bioresorbable. Therefore, modifying these polymers to tune their properties or functionalities is an effective strategy for developing biomedical materials.Poly(γ-glutamic acid) PGGAH is a biocompatible and biodegradable poly-γ-peptide with carboxylic side groups that can be substituted to modify the polymer’s properties. In this study, PGGAH was hydrophobically and cationically modified. Through hydrophobic modification and altering the hydrophilic properties, amphiphilic copolymers were produced, capable of self-assemble into nanoparticle systems for drug encapsulation and controlled release. This modification was carried out by partial esterification of carboxylate side groups with 4-phehyl-butyl bromide (4-PhBBr). Further decoration to produce stealth and targeting nanoparticles was achieved by reaction of some remaining carboxylate side groups with amino ended poly(ethylene glycol) (NH2-PEG) and NH2PEG derivatized with folic acid, respectively. Cationic modification of this biodegradable polymer enabled the formation of nanopolyplexes with DNA. This modification was carried out by esterification of carboxylate side groups with cationic 2-bromoethyl trimethylammonium bromide (BrETABr). The obtained derivatives were used to prepare nanoparticles through emulsion solvent evaporation or nanoprecipitation dialysis techniques. Nanoparticles with an approximate size of 100 to 380 nm were obtained, demonstrating their potential as drug delivery systems capable of encapsulating the anticancer drug doxorubicin.The chemical structure of the derivatives were characterized using proton and carbon-13 nuclear magnetic resonance (NMR) spectroscopy, and the physicochemical properties by gel permeation chromatography (GPC), and thermal gravimetric analysis (TGA). Functional group analysis was conducted through Fourier-transform infrared spectroscopy (FT-IR). Hydrolytic degradation was monitored by 1H NMR, while the appearance of the nanoparticles was observed using scanning electron microscopy (SEM), and their size and surface charge were determined by dynamic light scattering (DLS) and zeta potential measurements, respectively.For the hydrophobic copolymer series, cytotoxicity assays were carried out, confirming the low toxicity of the synthesized derivatives. Drug encapsulation and release was initially evaluated under physiological conditions, revealing that the release rate was higher in acidic pH and affected by the degree of polymer modification. On the other hand, cellular uptake nanoparticle tests demonstrated that the nanoparticles successfully penetrated cancer cells. The results of this study indicate that the biotechnological polymer PGGAH is a promising material for designing and developing biodegradable drug delivery systems with potential therapeutic applications for challenging diseases in pharmacological treatment.
  

DOCTORAL DEGREE IN SIGNAL THEORY AND COMMUNICATIONS

• HERNÁNDEZ CHULDE, CARLOS EFRÉN: Software defined networking for autonomous and secure optical networks
  
  Author: HERNÁNDEZ CHULDE, CARLOS EFRÉN
  Thesis file: (contact the Doctoral School to confirm you have a valid doctoral degree and to get the link to the thesis)
  Programme: DOCTORAL DEGREE IN SIGNAL THEORY AND COMMUNICATIONS
  Department: Department of Signal Theory and Communications (TSC)
  Mode: Normal
  Deposit date: 04/03/2025
  Reading date: 30/04/2025
  Reading time: 10:00
  Reading place: C4-028-2 de la EETAC - UPC (Campus CBL-Castelldefels)
  Thesis director: CASELLAS REGI, RAMON | MARTINEZ RIVERA, RICARDO VICTOR
  Thesis abstract: The increasing complexity and demands of modern telecommunications networks necessitate the development of autonomous and secure systems to ensure efficient, reliable, and secure communications. The integration of advanced technologies such as Artificial Intelligence (AI) and Machine Learning (ML) together with Quantum Key Distribution (QKD) into optical networks addresses these needs. This integration enables the creation of networks that can efficiently automate their operations while ensuring the highest standards of security. In this context, this thesis explores the use of Software Defined Networking (SDN) for the advancement of autonomous and secure optical networks, in particular Elastic Optical Networks (EONs). The research focuses on enhancing network efficiency and security to meet the growing complexity and demands for high-capacity, low-latency, and secure communications.The PhD thesis investigates the application of ML, specifically Deep Reinforcement Learning (DRL) and Graph Neural Networks (GNN) to tackle key challenges in the management and optimization of EONs. The primary goal is to develop autonomous and intelligent solutions for dynamic service provisioning, resource allocation, and spectrum management. A significant contribution of this work is the development of novel DRL-based approaches for Routing and Spectrum Assignment (RSA). These methods are designed to adaptively manage network resources in real-time, overcoming the limitations of traditional, static RSA algorithms. By considering latency as a key factor, the DRL-based RSA mechanism ensures the efficient provisioning of latency-sensitive applications and improves overall network performance metrics, such as latency and throughput. The thesis also examines the dynamic provisioning and optimal placement of Virtual Network Functions (VNFs) using DRL and GNNs. This combination of technologies enables a more efficient mapping of resource requirements to the physical infrastructure, facilitating scalable and flexible network management systems.The research also includes an experimental validation of the proposed solutions. A proof-of-concept (PoC) was implemented to demonstrate the integration of DRL models within an SDN control plane framework. This involved externalizing path computation to a dedicated entity that assists the SDN controller in the path and spectrum selection function. The experimental results confirmed the practical applicability of the DRL approach in supporting selected control functions in operational EON infrastructures.Furthermore, the research explores the coexistence of Continuous Variable Quantum Key Distribution (CV-QKD) and classical channels within EONs, which is essential for ensuring secure communications in the quantum computing era. To address the challenge of noise interference from high-power classical channels on sensitive quantum channels, the thesis introduces dynamic spectrum allocation strategies leveraging SDN. These strategies optimize the use of spectrum resources and minimize noise interference, ensuring secure and efficient operation of the integrated network.In summary, this thesis provides significant advancements in the field of autonomous and secure optical networks by integrating advanced ML techniques, contributing to the development of agile, high-capacity, reliable, and secure EONs for future telecommunications.
  

DOCTORAL DEGREE IN BUSINESS ADMINISTRATION AND MANAGEMENT

• TAGHI ZADEH ANSARI, EHSAN: TALENT MANAGEMENT IN SPANISH SME HOTEL SECTOR
  
  Author: TAGHI ZADEH ANSARI, EHSAN
  Thesis file: (contact the Doctoral School to confirm you have a valid doctoral degree and to get the link to the thesis)
  Programme: DOCTORAL DEGREE IN BUSINESS ADMINISTRATION AND MANAGEMENT
  Department: Department of Management (OE)
  Mode: Normal
  Deposit date: 14/04/2025
  Deposit END date: 29/04/2025
  Thesis director: FERNANDEZ ALARCON, VICENÇ
  Thesis abstract: Hospitality has a great role in economy of Spain as the second most visited country in the world. Human resources (HRs) are the most important resources in this industry. Talented employees can be competitive advantage of hospitality organizations and specially hotels as a key sub-sector of the industry. For managing talents, effective practices should be used for high potential and high performing employees, to fill in key positions. There is still a need to study talent management in certain sectors and small and medium-sized enterprises such as hospitality. Following the research question “Which are the talent management practices in SME hotels?”, we considered various objectives. The first ones describe the meaning of Talent and Talent Management by different hotel stakeholders. After that formal and informal talent management practices were identified in hotel sector. Comparing the different approaches and perceptions for talent management and corresponding practices in hotels was another objective that we followed up. Furthermore, Methodology is a critical part in research. Methodology of our research is empirical inductive and exploratory. Qualitative research was done. The sampling methods were a combination of handpicked, volunteering, and convenience models. We collected data from SME hotel HRs by semi structured interviews. We used platforms such as SABI and LinkedIn to reach the samples of research. The interviews were done based on a defined interview protocol. We used the software of Excel for data analysis. Regarding Ontology and Epistemology of research we can say that our effort was to find out what the truth is about talent management in hotels. What followed led us to search for a suitable method to understand how the impact of talent management is in hotels. Due to the nature of our research, interpretivism was more appropriate among other philosophical research paradigms. On the other hand, we used grounded theory and thematic analysis in the research. We started data analysis by defining demographics and doing initial data analysis including data cleaning, data screening and documentation. Following these steps, detailed data analysis was conducted based on grounded theory and thematic analysis. It is important to note that we have been assured of quality of the research by following the appropriate strategies of Trustworthiness, Validity and Reliability. Regarding the results, we can assert that concepts mentioned in literature review were compared with the results of interviews. We found the commonalities and differences of the concepts. Therefore, we prepared a list of key HR positions in hotels. Characteristics of talented people were discussed. Besides, a classified list of suitable practices for attraction, selection, training and performance of talented employees in hotel sector was provided from the perspective of various stakeholders. Goals of talent management were discussed. Finding people in charge of talent management tasks was another result achieved. In addition, we uncovered the factors influence talent management practices. From our point of view, responding to the research objectives is the most important part of research conclusions. Regarding the implications of the research, hotels especially small and medium sized ones can improve the situation of their talents and increase organization efficiency using the results of this project. The data collected and analysed can help managers to be more aware of the subject and to choose best fit practices in talent positions of their organization. In other words, the results increase job satisfaction and provide talented employees a better work situation. Furthermore, the results from interviews which are not justified in literature review are like new hypothesis that can be studied in the future research. Recommendations for researchers, practitioners and governments is another part we discussed.
  

DOCTORAL DEGREE IN CHEMICAL PROCESS ENGINEERING

• BARBERO DEL RÍO, MANUEL: Estudio de los principales contaminantes en lavandería industrial y diseño de un sistema de reutilización de agua
  
  Author: BARBERO DEL RÍO, MANUEL
  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: 24/04/2025
  Deposit END date: 08/05/2025
  Thesis director: DE PABLO RIBAS, JOAN | CASAS GARRIGA, SANDRA
  Thesis abstract: This doctoral thesis has enabled the development of a technological solution for water reuse in the industrial laundry sector. In addition, the emission of microplastics and emerging pollutants associated with this activity has been studied, focusing on strategies that minimise the problems associated with them.The reuse system, whose main technology is ceramic ultrafiltration, and uses foam fractionation and chemical oxidation as post-treatment, achieves a water reuse rate of 86% in unitary washing processes without affecting their final quality. The design process has been carried out in different phases, including analysis of the feasibility of the technologies, laboratory testing and validation at pilot and real scale.Foam fractionation was tested using an aeration column, which achieved surfactant separation rates of up to 70% from water and, as a result, a surfactant-concentrated stream whose reintroduction in subsequent washing processes improved the removal of certain pattern stains by up to 20%.Preliminary ultrafiltration membrane tests allowed the generation of explanatory models of permeate quality and transmembrane flux from a matrix of working pressure iterations for molecular cut-off sizes from 1 to 1400 kDa.For the pilot-scale validation of ceramic membrane ultrafiltration technology, a direct treatment-reuse process was combined and only 23L of mains water was used in each wash. This was implemented in a 13 kg capacity washing machine over 10 processes and demonstrated the greater efficiency of the 15 kDa membranes over the 50 kDa ones for the separation of anionic surfactants (+9%) and COD (+13%). The analysis of the rinsing efficiency showed more stable results with the use of 15 kDa membranes, which is associated with the greater removal of surfactants throughout the reuse cycles, so this pore size was chosen for the final validation of the system.Finally, ultrafiltration was combined with foam fractionation and chemical oxidation using ozone. These last two techniques coexisted in a cylindrical reactor built for this purpose and placed after ultrafiltration, which increased the removal efficiency of anionic surfactants to 91% after 15 minutes of treatment. In addition, a complete elimination of the colour released by the garments was confirmed, thus avoiding the transfer of this colour to subsequent washing processes.The emission of microplastics during the washing of garments throughout their useful life was estimated at 7,957,942 microfibres per kg of clothing washed. 44% of these microparticles are emitted during the first 5 washes and their emission can be substantially reduced with washing programmes with low mechanical action.The study of emerging contaminants in industrial laundry has shown the presence of this type of compounds in the wastewater generated. These would migrate from the fabrics where they are deposited or impregnated, to the water used in the washing process and would then be discharged into the sewage system. In total, 27 compounds of different types have been detected, such as disinfectants, medicines, antibiotics, drugs of abuse, pesticides and insect repellents. Hospital laundry has been found to be the sub-sector in whose water the highest concentrations and a greater variety of micropollutants have been detected, followed by hotel laundry and work clothes laundry. The elimination of this type of contaminant has been validated with a treatment system, achieving yields of over 98% for most of the compounds studied.
  
• PACHECO LÓPEZ, ADRIAN: Integrating modeling, synthesis, and knowledge management to support strategic decision-making toward the Circular Economy paradigm
  
  Author: PACHECO LÓPEZ, ADRIAN
  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: 16/04/2025
  Deposit END date: 02/05/2025
  Thesis director: GRAELLS SOBRE, MOISES | SOMOZA TORNOS, ANA
  Thesis abstract: This thesis tackles some of the most urgent global issues we face today: waste accumulation, resource scarcity, and climate change. The focus is on applying sustainable practices through models like Industrial Symbiosis (IS) and the Circular Economy (CE). Traditional linear economic models have hit a limit, and this research proposes a solution to optimize resource use and waste management. The central goal of this work is to develop a Decision Support System (DSS) to help identify and assess the most effective routes to convert waste into resources, particularly focusing on plastic waste, but with applications to other materials, too.Chapter 1 discusses how demographic and industrial trends are accelerating problems like waste accumulation, resource shortages, and climate crises. It emphasizes the urgent need to adopt models like CE and IS to reduce material consumption and waste generation. While implementing CE comes with challenges—such as figuring out the best ways to convert waste into valuable products—this chapter introduces the development of a DSS designed to support informed, rigorous decision-making for sustainable resource management.Chapter 2 dives into the methods and tools used to create the DSS. It introduces Process Systems Engineering (PSE), a crucial field for understanding and developing sustainable production processes. PSE uses tools like process modeling, simulation, and optimization to design more efficient and sustainable systems. This chapter explains how these tools can be applied to address the challenges of CE and help design sustainable processes.Chapter 3 presents a proof of concept for the application of CE, focusing on alternative fuels made from plastic waste. A techno-economic and environmental assessment compares plastic waste-derived fuels with traditional ones like diesel and gasoline. The results show that pyrolysis oil from plastic waste has a lower environmental impact and production cost compared to diesel. However, for gasoline substitutes, bioethanol and ethanol from plastic pyrolysis have mixed results. These findings highlight the potential of plastic waste conversion technologies, although more research is needed to improve fuel quality.Chapter 4 focuses on generating and evaluating new waste-to-resource routes, particularly for plastics. Using an ontological framework for more efficient knowledge management, this chapter creates a system to identify, generate, and classify processing alternatives for the upcycling of waste. The framework helps find the best ways to close material loops, and chemical recycling emerges as a promising option to reduce the environmental impact of plastic waste.Chapter 5 shows how an integrated set of tools was developed to design, model, and optimize waste conversion processes. Using methods like graph theory, network optimization, MCDM, and multi-objective optimization, the system helps identify and assess the best waste treatment routes. A case study on plastic waste highlights that chemical recycling for the recovery of raw materials could be a promising option from both an economic and environmental perspective.Chapter 6 concludes by summarizing the development and validation of the DSS. The thesis shows how the framework was successfully validated through case studies focused on plastic waste recovery, proving its effectiveness in closing material loops and supporting sustainable practices within the circular economy.
  

DOCTORAL DEGREE IN COMPUTER ARCHITECTURE

• PAVÓN RIVERA, JULIÁN: Turbo-boosting Vector Architectures For Applications With Irregular Memory Access Patterns
  
  Author: PAVÓN RIVERA, JULIÁN
  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: 24/04/2025
  Deposit END date: 08/05/2025
  Thesis director: CRISTAL KESTELMAN, ADRIAN
  Thesis abstract: Since we are reaching the boundaries of the trifecta of Von Neumann architectures, Moore’s Law and Dennard’s scaling, both software developers and hardware architects are forced to find better and novel approaches to exploit parallelism to improve the processing time of commodity and High Performance Computing (HPC) CPUs on the vast amount of data generated daily.As Von Neumann architectures, Moore’s Law, and Dennard’s scaling near their limits, new strategies are needed to improve the performance and efficiency of CPUs for data-intensive workloads. While ILP and TLP are widely explored, there is still untapped potential in Data-Level Parallelism (DLP), which vector architectures leverage via SIMD execution. These architectures perform well in workloads with regular memory access, such as dense linear algebra or image processing. However, many modern applications—including sparse linear algebra, databases, and genome analysis—exhibit irregular access patterns, leading to poor performance on commercial vector systems like AVX-512 and ARM SVE.Irregular workloads face three main challenges: (i) large memory footprints that cause frequent off-chip accesses and memory-bound behavior, (ii) underutilized memory bandwidth due to poor spatial locality, and (iii) low ILP and vector unit underutilization from fragmented access patterns. This thesis addresses these issues by introducing three vector acceleration frameworks tailored to these domains: VIA, VAQUERO, and QUETZAL.We begin with sparse linear algebra, where indirect accesses and sparsity hinder performance despite software optimizations like tiling. To address this, we propose VIA, a vector architecture with a tightly-coupled scratchpad memory that enhances locality and bandwidth utilization. VIA improves performance in key kernels (e.g., SpMV, SpMM) over leading libraries.Database workloads share irregularity but involve larger working sets and rely on different locality techniques. These patterns limit VIA’s effectiveness. We therefore propose VAQUERO, a vector accelerator with a scratchpad coherent with the CPU cache, enabling efficient processing of large-scale queries like joins and aggregations.In genome sequence analysis, long-read technologies demand high-throughput, low-latency processing. These workloads involve highly irregular and unaligned memory access, often with compacted data formats. To address this, we present QUETZAL, a vector architecture with hardware support for unaligned accesses and new vector instructions optimized for sequence alignment and edit distance computation.All three accelerators are implemented at RTL and synthesized for a 7nm node. VIA achieves a 5.5x speedup in sparse algebra, VAQUERO a 2.7x speedup in databases, and QUETZAL a 5.7x boost in genome analysis, all with only ~1% area overhead. Together, these designs show how targeted architectural innovation can close the performance gap for irregular workloads across key computational domains.
  

DOCTORAL DEGREE IN COMPUTING

• FLORES HERRERA, JAVIER DE JESÚS: A Framework to Operationalize and Automate the Data Integration Lifecycle
  
  Author: FLORES HERRERA, JAVIER DE JESÚS
  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: 14/04/2025
  Deposit END date: 29/04/2025
  Thesis director: ROMERO MORAL, OSCAR | NADAL FRANCESCH, SERGI
  Thesis abstract: Data plays a key role in today’s world. Many organizations collect and store massive amounts of data from many different data sources. As a result, these data collections show a diversity in structure and semantics that grows as the data sources expand and evolve. These factors challenge traditional data management methods, which depend on fixed structures and stable conditions. There is a mismatch between old assumptions and new realities, where it is not enough to just collect data and run conventional tools. Instead, we must rethink how we integrate data to support high variety, handle large-scale collections, and accommodate new available data. This PhD thesis proposes innovative and advanced techniques to support and automate the data integration lifecycle. First, we describe how to represent and standardize data sources using graph-based schemas. These schemas provide a solid foundation for all steps of the data integration lifecycle. Next, we introduce an integration method that leverages graph-based schemas to add new data incrementally without disrupting existing integration structures. This approach ensures that data integration remains flexible and scalable as organizations grow. We also help users find the right datasets to integrate. By focusing on data discovery, we reduce the time spent exploring irrelevant data sources and suggest relevant ones for integration. To this end, we focus first on facilitating the discovery of joinable attributes among datasets. We propose a new qualitative metric and use data profiles and learning models to decide which attributes are worth joining. To further enhance data discovery, we introduce contextual pre-filtering. Using data profiles and graph-based schemas, we can focus on promising datasets before applying data discovery tools. This pre-filtering step not only boosts the accuracy of existing data discovery tools but also optimizes their performance by narrowing the search space. In summary, this thesis helps bridge the gap between conventional data methods and modern, diverse data ecosystems. The results contribute to the field of data integration by offering scalable and automated solutions that match the changing needs of data integration today.
  

DOCTORAL DEGREE IN ENVIRONMENTAL ENGINEERING

• PASTOR LÓPEZ, EDWARD JAIR: Nature-based solutions to reduce the spread of antibiotics and antimicrobial resistance genes in aquatic ecosystem
  
  Author: PASTOR LÓPEZ, EDWARD JAIR
  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: Article-based thesis
  Deposit date: 14/04/2025
  Deposit END date: 29/04/2025
  Thesis director: MATAMOROS MERCADAL, VÍCTOR | ESCOLÀ CASAS, MÒNICA
  Thesis abstract: Antibiotics (ABs) are antimicrobial agents whose production and consumption have increased exponentially since the discovery of penicillin in 1929. The overuse of ABs has driven the emergence of antibiotic resistance, leading the frequent detection of ABs and antimicrobial resistance genes (ARG) in aquatic environments, primarily due to wastewater treatment plants (WWTP) effluent discharge despite regulatory efforts. Additionally, prolonged extreme weather conditions, such as droughts, intensify this issue by reducing water availability, threatening aquatic ecosystems and human health. Although advanced water treatment technologies, such as ozonation or membrane-based systems, can remove these pollutants from wastewater, their high cost of construction and maintenance, limited their widespread implementation. Alternatively, Nature-Based Solutions (NBS) have emerged as a potential option due to their cost-effectiveness and their potential capacity to remove a wide range of pollutants. However, studies on the reduction of ABs and ARGs in full-scale on NBS applied to wastewater treatment or river streams remain limited.This PhD dissertation is presented as a compendium of publications and evaluates the effectiveness of NBS in reducing ABs and ARGs in wastewater. First, a review study explored the capacity of NBS to reduce the presence of ABs, ARGs and pathogens across diverse aquatic environments spanning secondary wastewater treatment to estuarine areas and saltmarshes (Chapter II – DOI: 10.1016/j.scitotenv.2024.174273). Second, the performance of two full-scale configurations of constructed wetlands (CW) as tertiary wastewater treatment systems were monitored during the summer and the winter seasons to assess the reduction of ABs and ARGs (Chapter III – DOI: 10.1016/j.watres.2024.122038). Their effectiveness were compared with a conventional tertiary wastewater treatment technology system. Third, the impact of wastewater effluent-dominated stream renaturalization on the reduction of ABs and ARGs across seasonal variations was assessed by monitoring a vegetated and less vegetated stream during both warm and cold periods (Chapter IV – DOI: 10.1016/j.envres.2025.120910).The findings presented in this doctoral research project demonstrate that NBSs are potential alternatives for water treatment management in river basins. CWs as tertiary wastewater-treatment systems have shown the capacity to improve the general water quality parameters and remove ABs and ARGs. In addition, unlike conventional systems, those systems promote a shift in microbial composition towards a more natural profile and reduce the ecotoxicological and resistance selection risks more than a conventional tertiary WWTP. Furthermore, vegetated streams with meanders have shown to increase the degradation kinetics of ABs and the attenuation of ARGs, foster gradual changes in bacterial community structures and decrease the ecotoxicological and resistance selection risks, especially during the warm period. Further research should keep focusing on evaluating novel full-scale NBS configurations, identification of the transformation products (TPs) as well as other aquatic micropollutants, quantification of diverse ARGs and assessing the ecological status of the water.
  

DOCTORAL DEGREE IN PHOTONICS

• AMUAH, EMMANUEL BAFFU: Probing structural coherence across a light-induced double phase transition
  
  Author: AMUAH, EMMANUEL BAFFU
  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: 14/04/2025
  Deposit END date: 29/04/2025
  Thesis director: WALL, SIMON ELLIOT | JOHNSON, ALLAN STEWART
  Thesis abstract: Strongly-correlated materials have emerged as one of the most active areas of research in Condensed Matter Physics. Interests in these materials arise mainly from the pliability of their properties, offering the possibility of tailoring these materials for specific applications. This is, in turn, due to the rich interplay of interactions between electronic, orbital and lattice degrees of freedom. This complex coupling of the different degrees of freedom, on the other hand, makes strongly-correlated materials difficult to understand.Ultrafast spectroscopy offers the possibility of resolving this bottleneck and provides insight into aspects of correlated materials crucial for enhancing our understanding of these materials. One such aspect is photoinduced phase transitions, where light drives a symmetry change in a material. To date, research has focused on using light to force materials to cross a single structural transition. In this work, we investigate the possibility of making multiple phase jumps with a single pulse of light. A suitablesystem for such study is the manganite, Pr0.5Ca1.5Mn04, which despite its prospects remains less explored. This layered manganite exhibits multiple phase transitions of electronic, orbital and structural origins, as a function of temperature. The presence of more than one phase transition in Pr0.5Ca1.5Mn04 allows us to examine the possibility and mechanism of multi-phase transition, an aspect of photoinduced phase transition that has hitherto not received much attention. The physics of the manganites is strongly dictated by the dynamics of Jahn-Teller phonons, which occur at a very high frequency (>15 THz). Studies involving these phonons thus call for setups with a very high time resolution.This thesis first discusses the construction of a novel setup that makes use of few-cycle pulses from the visible to the near infrared wavelength regions. Then, leveraging on the capabilities of this setup, we undertake ultrafast measurements on Pr0.5Ca1.5Mn04 in two parts: the linear and nonlinear pumping regimes. In the linear regime, we perform broadband, low-fluence measurements to characterize the sample. From this, we identify key structural and electronic changes that occur during the thermal transition pathway, allowing us to map out the sample into different symmetry regions, in agreement with literature. In the nonlinear pumping regime, we study the fluence dependence of the changes identified from the linear regime. By analyzing the coherent lattice response, we find indications of both single and double phase transitions occurring.
  

DOCTORAL DEGREE IN SIGNAL THEORY AND COMMUNICATIONS

• DÍEZ GARCÍA, RAÚL: New Methods for Radio Frequency Interference Mitigation in Microwave Radiometry
  
  Author: DÍEZ GARCÍA, RAÚL
  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: 24/04/2025
  Deposit END date: 08/05/2025
  Thesis director: CAMPS CARMONA, ADRIANO JOSE
  Thesis abstract: Radio Frequency Interference (RFI) has emerged as the most critical challenge to the effective exploitation of the microwave spectrum for Earth observation. RFI consists of non-natural emissions that obscure the radiometric signals measured by microwave radiometers. The presence of RFI degrades the accuracy and completeness of radiometric measurements. This is evident, for example, in the European Space Agency’s (ESA) Soil Moisture and Ocean Salinity (SMOS) mission, which, despite operating in the protected L-band, is severely affected by interference. This PhD thesis presents innovative approaches to mitigate this issue, with a particular focus on Synthetic Aperture Interferometric Radiometers, which have been insufficiently studied in the context of RFI.This PhD thesis introduces several novel RFI detection and mitigation techniques, including autocorrelation-based detection, non-linear decomposition using Empirical Mode Decomposition (EMD), and data-adaptive mitigation via the Karhunen-Loève Transform (KLT). These methods address some of the limitations of existing approaches and have been evaluated with simulated data using standard performance metrics. Furthermore, a new algebraic interpretation of mitigation is provided, bridging the gap between RFI detection and signal transformation methods, and introducing novel performance evaluation metrics.In addition to the theoretical contributions, this thesis includes experimental validation of the proposed techniques using real-world data. The practical feasibility and performance of whiteness-based detection and KLT-based mitigation techniques are demonstrated in realistic scenarios. The research also explores the effects of signal quantization on mitigation techniques, a relevant concern for digital radiometer implementations.
  

DOCTORAL DEGREE IN SUSTAINABILITY

• LEDUCHOWICZ MUNICIO, ALBA: Multicriteria methodology for assessing the sustainability of last-mile electrification programs
  
  Author: LEDUCHOWICZ MUNICIO, 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 SUSTAINABILITY
  Department: University Research Institute for Sustainability Science and Technology (IS.UPC)
  Mode: Article-based thesis
  Deposit date: 14/04/2025
  Deposit END date: 29/04/2025
  Thesis director: DOMÉNECH LÉGA, BRUNO | FERRER MARTI, LAIA
  Thesis abstract: The sustainability-driven global energy transition requires ensuring universal access to electricity, particularly in last-mile rural areas. Renewable-based energy access programs are pivotal for fostering development, empowerment, and climate resilience in these regions. However, comprehensive assessments of past efforts are crucial to avoid repeating mistakes and ensure operational success of these initiatives. Likewise, it is particularly important to consider the durability and impact of the implemented solutions in terms of development and gender equality. This PhD thesis aims to address these gaps by developing multi-criteria procedures for a holistic sustainability assessment of last-mile electrification initiatives in emerging economies, while also extracting lessons from real-world case studies. The research first analyses the local energy transition for two historically marginalized populations: indigenous and traditional communities. It also assesses large-scale electrification programs in Brazil and Venezuela, and subsequently evaluates the operation and region-wide impacts in three Brazilian states. Additionally, a framework using gender data and sex-disaggregated data is proposed to ensure gender-responsive electrification initiatives, with case studies from three Brazilian municipalities. These phases underscore the importance of tailored multicriteria decision-making analysis frameworks that consider local contexts, stakeholder preferences, and inclusive perspectives. Assessment of last-mile electrification outcomes reveals the transformative potential of renewable energy solutions, emphasizing the need for systemic and integrated approaches and multi-stakeholder collaboration to achieve Sustainable Development Goals. This analysis serves as a guide for last-mile electrification promoters to synergistically address sustainable design, operational durability and long-term local development that leaves no one behind.