Thesis authorised for defence

DOCTORAL DEGREE IN AGRI-FOOD TECHNOLOGY AND BIOTECHNOLOGY

BISCAMPS DALMAU, JORDI: Generació de mapes de vigor mitjançant sensors remots i proximals per a l’aplicació a dosis variable de productes fitosanitaris en vinya utilitzant tecnologia de modulació d’ample de pols (PWM).
Author: BISCAMPS DALMAU, JORDI
Programme: DOCTORAL DEGREE IN AGRI-FOOD TECHNOLOGY AND BIOTECHNOLOGY
Department: Department of Agri-Food Engineering and Biotechnology (DEAB)
Mode: Normal
Deposit date: 11/09/2026
Reading date: 03/12/2026
Reading time: 10:00
Reading place: Sala de Graus de l'EEABB, Campus del Baix Llobregat de la UPC, C. Esteve Terradas 8, Ed. D4, Castelldefels
Thesis director: GIL MOYA, EMILIO | GARCIA RUIZ, FRANCISCO JOSE
Thesis abstract: This doctoral thesis contributes to the development and validation of variable-rate technologies for the application of plant protection products in vineyards through flow-rate control systems based on pulse-width modulation (PWM) and vegetation characterization methods using remote and proximal sensors. First, a prototype based on PWM valves was developed and validated. The system used prescription maps generated from multispectral imagery acquired with an unmanned aerial vehicle to identify vigor zones and automatically adjust the flow rate. Trials were conducted in both trellised and goblet-trained vineyards at two phenological stages. The valves showed an almost linear response over a duty-cycle range of 20-80%. Under field conditions, the system reduced the spray volume applied by up to 23.8%, with an average reduction of 18.6%, without significant differences in normalized deposition compared with the conventional treatment. Second, three application strategies were compared: a conventional constant-volume system, a pressure-controlled variable-rate system, and the PWM-based system. The results showed that working pressure and the resulting droplet spectrum had a greater influence on spray drift than the total volume applied. The conventional system generated ground losses close to 40% under low-vigor conditions, whereas the PWM system reduced both ground losses and airborne drift under all the conditions studied. Finally, a device for high-throughput vegetation characterization was designed using ultrasonic sensors, GNSS, and an onboard control unit. The system was validated under laboratory and field conditions and compared with three-dimensional models generated using UAV photogrammetry. Both methods provided comparable estimates of the main structural parameters of the vineyard, with mean absolute percentage errors below 10%. Although photogrammetry offered higher spatial resolution, ultrasonic sensors proved to be a robust and operational alternative under field conditions.

DOCTORAL DEGREE IN ARCHITECTURAL, BUILDING CONSTRUCTION AND URBANISM TECHNOLOGY

REYNA RUIZ, CATALINA: Producción de bloques de tierra comprimida (BTC) para climas cálidos y húmedos con maquinaria adobera, y el establecimiento del agente estabilizador óptimo para el cumplimiento de los códigos locales.
Author: REYNA RUIZ, CATALINA
Programme: DOCTORAL DEGREE IN ARCHITECTURAL, BUILDING CONSTRUCTION AND URBANISM TECHNOLOGY
Department: Department of Architectural Technology (TA)
Mode: Article-based thesis
Deposit date: 21/09/2026
Reading date: 12/11/2026
Reading time: 16:30
Reading place: ETSAB (Esc.Téc.Sup.Arquit.Bcn)-Pl. Baja-Sala GradosAv. Diagonal, 649Enlace a videoconferencia: https://meet.google.com/nyr-vdqm-jboConnexión 16.00h
Thesis director: GOMEZ SOBERON, JOSE MANUEL VICENTE | ROJAS VALENCIA, MARIA NEFTALI
Thesis abstract: ABSTRACTAs world population grows so does the already existing housing deficit and it becomes evident that the construction industry has a responsibility to mitigate its environmental impacts that come from the direct extraction of raw materials, their transformation and the transportation. On the other hand, the implementation of climate control systems increases the energy demand.Simultaneously there is a global increase of the production of different waste materials that if not treated pose a great challenge to maintain ecosystems healthy. In the case of the United States, the annual volume of the construction and demolition residues as well as the glass that are not recycled is equivalent to 20,000 and 546 bases of the Statue of Liberty respectively.Return the gaze to the earth, is not a beautiful metaphorical phrase but a proposal for the construction industry to opt for materials with a reduced carbon footprint that can be sourced locally to reduce it transportation impact and that offer a greater insulation capacity. Those are the main characteristics of the Compressed Earth Blocks (CEB). If these include closed loop materials in their formulation; a greater sustainability is achieved.This research work studied the incorporation of local soils and aggregates as well as two closed loop materials in the production of Compressed Stabilized Earth Blocks (CSEB). One group with lime and another with cement in three different concentrations by weight (5, 10 and 15%). They were manufactured with a semi-automatic machine in a locality that had average temperature of 32 °C and 91% relative humidity. Four matrices were design per type of stabilizer, the first one was considered the one of reference and included the soils and the natural aggregates, for the second one, natural sand was replaced with recycled glass sand, for the third one the natural aggregates were replaced with the crushed concrete potion of construction and demolition wastes and the fourth one incorporated both closed loop materials (24 formulations). In the case of the CSEB with lime, 92% of the formulas exceeded the simple compression test (SCT) and capillary water absorption (AbsCoeff10) values established by the normativity. The highest SCT obtained was 4.3 MPa. For the CSEB with cement, a broader set of characteristics were studied; such as apparent porosity (AP) – immersion test, modulus of elasticity (MoE) and thermal conductivity (TC) in the second phase of the research and additionally, for the third one; thermogravimetric analysis (TGA), scanning electron microscopy (SEM), and spectrometry of dispersed X-ray energy (EDS). With an analysis of optical images (OIA) a method was presented to correlate the formulas with some of the properties, including predicting capabilities with mathematical equations. Analyzing SEM images ITZ width values and porosity values were obtained. The greatest SCT value registered was 7.3 MPa and the lowest values of AbsCoeff10 and CT were 8 g/(cm² × min0.5) y 0.684 W/m·K. The formulas with 15% of cement showed the biggest mass loss, associated to the presence of interlayer water and the hydration products as well as the greater porosity and ITZ width reduction with the highest concentrations of silicon and calcium.

DOCTORAL DEGREE IN ARCHITECTURAL, CIVIL AND URBAN HERITAGE AND REFURBISHMENT OF EXISTING BUILDINGS

GARCIA SALANOVA, ANTONIO: Arquitectura amb propòsit per a una gestió sostenible dels equipaments públics
Author: GARCIA SALANOVA, ANTONIO
Programme: DOCTORAL DEGREE IN ARCHITECTURAL, CIVIL AND URBAN HERITAGE AND REFURBISHMENT OF EXISTING BUILDINGS
Department: Departamento de Representación Arquitectónica (RA)
Mode: Normal
Deposit date: 09/09/2026
Reading date: 18/11/2026
Reading time: 10:30
Reading place: ETSAB (Escuela Técnica Superior de Arquitectura de Barcelona) - Planta -1 - Aula C-B11Av. Diagonal, 649-651 - 08028 - Barcelona
Thesis director: REDONDO DOMINGUEZ, ERNEST
Thesis abstract: PURPOSEFUL ARCHITECTURE FOR THE SUSTAINABLE MANAGEMENT OF PUBLIC FACILITIESThis research analyses, formulates and reasonably contrasts a comprehensivea model of sustainable management of public facilities throughout their life cycle, from planning and design to operation and dismantling, and incorporates data-driven governance aimed at facilitating collaborative decision-making between the different agents involved. It is based on the hypothesis that the adoption of a comprehensive sustainable management model, based on collaborative digital methodologies —especially Building Information Modeling (BIM)—, sustainability criteria and life cycle analysis (LCA), makes it possible to overcome the limitations of traditional models of management of public facilities, characterized by the fragmentation of processes, the lack of traceability of information and the low optimization of the use phase. The work adopts a mixed methodological approach that combines systematic review of the literature, comparative analysis between management models, interviews with experts from the public and private sectors, quantitative validation and the development of an original methodological instrument: a methodological framework of 100 sustainable technical criteria applicable to the entire life cycle, which operationalizes the proposed management model. Unlike predominantly theoretical approaches, the research is carried out in a real context of public management of educational facilities and incorporates recent data and professional experiences (2024–2025), especially in relation to the implementation of BIM in the operation phase.The research also develops a practical case, a hypothetical Audiovisual Vocational Training Institute of Catalonia in the Tres Xemeneies del Besòs site, which acts as a proof of concept of the proposed comprehensive management model. This case makes it possible to contrast the coherence and feasibility of the model, as well as its architectural translation in relation to the pedagogical requirements of vocational education.The results positively show the three hypotheses formulated. Firstly, the comprehensive management model based on BIM–LCA improves the efficiency, quality and sustainability of processes. Secondly, its application determines a new flexible, modular architectural typology aimed at efficient maintenance. Thirdly, the integral model is adapted in a coherent way to the functional and pedagogical requirements of Vocational Training, reinforcing the connection between architecture, management, educational innovation and participatory governance processes. Of particular relevance is the contribution in the field of digital operation management, still underdeveloped in current institutional practice,Finally, the research proposes future lines aimed at data-driven exploitation, the integration of intelligent systems and the development of new professional profiles linked to life cycle management.

DOCTORAL DEGREE IN AUTOMATIC CONTROL, ROBOTICS AND VISION

CRUZ RANGEL, DAVID ANTONIO: Electrical-Current-based Physical Feature Extraction and Online Condition Assessment for Rotodynamic Systems Monitoring
Author: CRUZ RANGEL, DAVID ANTONIO
Programme: DOCTORAL DEGREE IN AUTOMATIC CONTROL, ROBOTICS AND VISION
Department: Department of Automatic Control (ESAII)
Mode: Normal
Deposit date: 17/09/2026
Reading date: 06/11/2026
Reading time: 11:00
Reading place: Aula 28.8, Escola Tècnica Superior d'Enginyeria Industrial de Barcelona (ETSEIB) Campus Diagonal Sud, Av. Diagonal, 647 08028 Barcelona
Thesis director: OCAMPO MARTINEZ, CARLOS AUGUSTO | DÍAZ ROZO, JAVIER
Thesis abstract: This dissertation investigates the use of electrical current measurements as a non-intrusive observation tool for industrial systems. The work is motivated by the need for scalable, cost-effective, and online monitoring solutions able to extract meaningful information from industrial assets without extensive dedicated instrumentation. The thesis focuses on rotodynamic systems and nonlinear electromechanical processes, where mechanical, hydraulic, electrical, and process-related phenomena are indirectly embedded in current signals.Conventional condition monitoring commonly relies on dedicated sensors such as accelerometers, pressure transducers, flow meters, acoustic sensors, or temperature probes. Although these sensors provide valuable information, their deployment may increase cost, complexity, maintenance requirements, and intrusiveness, particularly when many assets must be monitored continuously. In contrast, electrical current can be acquired non-intrusively using clamp-on sensors and is widely available in electrically driven systems, making it a promising information source for industrial monitoring.The central hypothesis of this thesis is that current signals contain information beyond energy consumption. Through electromechanical coupling, variations in mechanical load, hydraulic disturbances, operating regimes, degradation processes, and process dynamics can modify the electrical response of the system. Therefore, with suitable signal-processing, feature-extraction, and condition-assessment methods, current measurements can be transformed into a practical observation modality for online monitoring.The thesis is organized into four parts. Part I introduces the motivation, research questions, and state of the art in online Non-Intrusive Load Monitoring (NILM) and current-based industrial monitoring, emphasizing limitations related to physical interpretability, non-stationary operation, computational cost, scalability, and edge deployment. Part II develops the core methodologies for physical feature extraction, characterization, and identification from current measurements. First, a PSD-based current signature analysis method is proposed for variable-speed rotodynamic systems, enabling the extraction of mechanically meaningful degradation indicators under non-stationary conditions. The framework is then extended to hydraulic phenomena in centrifugal pumps by combining positive-sequence magnitude analysis and rotational-frequency-normalized PSD representations for cavitation detection. Finally, a non-intrusive framework is developed for coupled electromechanical systems operating under aggregated electrical measurements, enabling load identification, activation tracking, and health-state assessment.Part III integrates the proposed methodologies into online monitoring frameworks for industrial deployment. An online current-signature-based condition assessment approach is developed for HVAC water pumps, demonstrating the feasibility of cavitation monitoring under real operating conditions. The final framework is deployed in an industrial hydroelectric turbine, where electrical, mechanical, and hydraulic phenomena are monitored using only three-phase current measurements. This deployment demonstrates the practical applicability of the proposed methodology within an edge-computing architecture and under realistic constraints.Overall, this dissertation advances a unified, scalable, and physically interpretable current-based monitoring framework for industrial systems. The developed methodologies show that electrical current measurements can go beyond energy monitoring or load identification, serving as a rich source of information about mechanical degradation, hydraulic anomalies, process dynamics, and condition assessment. These findings support current signals as a versatile, non-intrusive diagnostic tool, especially where traditional sensors are costly, impractical, or difficult to maintain.
PEÑA CAMPOS, JOHAN SEBASTIAN: Interpretable Decision Support Systems for Early Cancer Diagnosis and Personalized Treatment
Author: PEÑA CAMPOS, JOHAN SEBASTIAN
Programme: DOCTORAL DEGREE IN AUTOMATIC CONTROL, ROBOTICS AND VISION
Department: Department of Automatic Control (ESAII)
Mode: Change of supervisor
Deposit date: 22/09/2026
Reading date: 04/12/2026
Reading time: 11:00
Reading place: Aula 28.8 del ETSEIB, Escola Tècnica Superior d'Enginyeria Industrial de Barcelona (ETSEIB) Campus Diagonal Sud, Av. Diagonal, 647, 08028 Barcelona
Thesis director: OCAMPO MARTINEZ, CARLOS AUGUSTO | PATIÑO GUEVARA, DIEGO ALEJANDRO
Thesis abstract: Cancer remains a leading cause of death worldwide, with gastric and metastatic prostate cancer imposing substantial burdens, particularly in low- and middle-income countries. This thesis develops interpretable Decision Support Systems (DSS) for two phases of cancer care: early diagnosis and personalized treatment optimization.The first stage targets early diagnosis of gastric cancer using routinely collected data from the Colombian health system (SGSSS). A deep learning architecture combines bidirectional Long Short-Term Memory (LSTM) networks and multi-head attention to process sequences of diagnostic codes and procedures from over 4.9 million patient trajectories, with Spanish biomedical embeddings, intelligent undersampling and Focal Loss addressing the extreme class imbalance (5103:1). It is then reformulated as a more parsimonious 2D convolutional network (CNN-2D) with demographic features, adopted as the final model for its interpretability, parsimony (~176k vs ~4.5M parameters) and competitive performance (AUC-ROC ≈ 0.835). The Nonlinear Oblique Subspace Projections (NObSP) methodology is extended to dynamic systems to interpret the model, decomposing predictions into clinically meaningful components (reconstruction error below 5%) and separating genuine clinical signal, an enriched upper-gastrointestinal pattern and a coherent age threshold, from geographic and utilization confounds.The second stage addresses personalized treatment for metastatic castrate-resistant prostate cancer (mCRPC) by integrating Evolutionary Game Theory (EGT) and Model Predictive Control (MPC). Lotka-Volterra competition models capture frequency-dependent selection among phenotypically distinct cell populations under therapy, and MPC optimizes adaptive schedules balancing tumor burden, resistance, toxicity and parsimony. For single-agent Abiraterone, it extends the median Time to Progression (TTP) from 275 days under continuous Maximum Tolerated Dose (MTD) to 420 days, roughly a 50% improvement over continuous MTD, while using about one fifth of its cumulative drug dose, and it outperforms statically optimized schedules (344 and 340 days). Dual-agent formulations coordinating Abiraterone and Docetaxel delay resistant-population dominance beyond 8,000 days, about 50% longer than the best adaptive-therapy baseline of West et al., while a blocking strategy cuts computation by over 98% versus recomputing at every step.The framework is further extended by introducing replicator dynamics, the canonical equation of EGT, as an alternative prediction model, deliberately creating a structural mismatch with the Lotka-Volterra plant while keeping the controller, objective and decision criteria unchanged. The replicator-based controller yields smoother single-agent dosing with equivalent clinical outcomes, and a minimal intervention, an 81% reduction in cumulative exposure relative to the matched Lotka-Volterra controller, suffices for sustained tumor control over the full simulation horizon in the dual-agent case, with 94% less computation than that controller. This gain follows from the modeling choice, not a different control law.Both stages treat interpretability as a core design requirement, operate within resource-constrained settings and establish validation frameworks that open pathways toward clinical evaluation, showing how mathematical and computational methods can be applied systematically across cancer care to improve patient outcomes.

DOCTORAL DEGREE IN BIOMEDICAL ENGINEERING

CLARAMUNT MOLET, MIREIA: Development of a novel clinical gait assessment tool for monitoring and stratifying neuromuscular diseases
Author: CLARAMUNT MOLET, MIREIA
Programme: DOCTORAL DEGREE IN BIOMEDICAL ENGINEERING
Department: Department of Mechanical Engineering (EM)
Mode: Normal
Deposit date: 24/07/2026
Reading date: 17/11/2026
Reading time: 10:30
Reading place: Aula 28.8. Planta 1, Escola Tècnica Superior d'Enginyeria Industrial de Barcelona (ETSEIB), Av. Diagonal, 647, 08028 Barcelona
Thesis director: FONT LLAGUNES, JOSEP MARIA | IDELSOHN ZIELONKA, SEBASTIAN
Thesis abstract: Neuromuscular diseases are chronic and degenerative conditions of the skeletal musculature that progressively impair gait, one of the primary determinants of functional independence. Their monitoring, both in clinical practice and clinical trials, relies on clinical scales and functional tests that are subject to observational bias, ceiling effects, and insufficient sensitivity to detect subtle changes. At a time when novel disease-modifying therapies are emerging, the availability of an objective, portable tool integrable into routine clinical practice for biomechanical mobility assessment represents an unmet clinical need.To address this need, the present thesis develops the biomechanical algorithms to derive spatiotemporal parameters, sagittal joint kinematics, muscle activation, plantar pressure distribution, and heart rate from raw wearable sensor data. Data capture relies on the Ephion Mobility system, which integrates and synchronises inertial sensors, surface electromyography, plantar pressure insoles, and a cardiac sensor within a single mobile application. Using this system, data were collected from 176 volunteers, 90 patients and 86 controls, across two countries and standard hospital settings.The first study, conducted in collaboration with Hospital Sant Joan de Déu, serves as the proof of concept. It validates a multidimensional gait score for Duchenne muscular dystrophy, derived from multisensor data through unsupervised learning, which demonstrates good agreement with current clinical metrics and promising capacity for disease monitoring.With the validity of the approach established, the second study, carried out at the Copenhagen Neuromuscular Center, explores its applicability in two conditions with markedly different distributions of muscle weakness: Becker muscular dystrophy and myotonic dystrophy type 1. Biomechanical gait assessment is shown to discriminate disease-specific patterns and severity gradients that functional tests do not capture. Finally, the third study, conducted in collaboration with the Spanish Pompe Patient Association, addresses the most demanding question: whether the tool can detect longitudinal progression in a slowly evolving disease. The Pompe-MDPI achieves statistical significance over one year of follow-up in a context where current metrics fail to detect change over the same interval.Taken together, the results provide preliminary evidence that portable multidimensional biomechanical assessment is clinically feasible, sensitive to functional severity, and capable of detecting longitudinal disease progression where conventional measures fall short. The derived scores represent promising candidates as digital biomarkers for natural history studies and clinical trials of disease-modifying therapies.
GARCÍA HERNANDO, GABRIEL: Development of an advisory system for the administration of anaesthetics and analgesics
Author: GARCÍA HERNANDO, GABRIEL
Programme: DOCTORAL DEGREE IN BIOMEDICAL ENGINEERING
Department: Department of Automatic Control (ESAII)
Mode: Normal
Deposit date: 18/09/2026
Reading date: 27/11/2026
Reading time: 11:00
Reading place: Aula 28.8, Escola Tècnica Superior d'Enginyeria Industrial de Barcelona (ETSEIB), Campus Diagonal Sud, Av. Diagonal, 647 08028 Barcelona
Thesis director: VALLVERDU FERRER, MARIA MONTSERRAT | GONZÁLEZ PIJUAN, CARMEN
Thesis abstract: Current technology provides valuable indices of hypnotic state, and although adverse anaesthesia effects have declined, important challenges remain. In particular, distinguishing between pharmacologically distinct anaesthetic agents and reliably predicting and detecting intraoperative nociception from physiological signals would support more personalized perioperative care.Electroencephalography (EEG) and heart rate variability (HRV), derived from RR intervals, provide complementary information on brain and autonomic responses to anaesthetic drugs and noxious stimuli. However, the optimal parameterisation of nonlinear descriptors, such as time delay, symbolic encoding, frequency band, and analytical signal representation, has not been systematically explored for drug discrimination and nociception assessment.This thesis pursues two objectives: first, discrimination between three pharmacological states, no drug (ND), propofol (P), and remifentanil (R), using EEG-based symbolic dynamics and Poincaré plot analysis; and second, prediction and detection of intraoperative nociception, defined as movement response to a noxious stimulus, using Poincaré analysis of EEG and RR signals.A total of 599 patients under general anaesthesia were analysed. For drug discrimination, 84 patients met stability criteria (28 ND, 28 P, 28 R). For nociception analysis, 163 patients yielded 329 stimulus instances (159 movers, 170 non-movers). At the patient level, 55 were consistent movers, 72 consistent non-movers, and 36 showed mixed responses.EEG signals were decomposed into seven frequency bands. For each band, three Hilbert-based representations were derived: amplitude, envelope, and phase. The first derivative was also computed for symbolic dynamics. Poincaré descriptors and symbolic features were extracted across all bands and representations, forming a multidimensional feature space applied consistently to both analyses.Symbolic dynamics parameters (number of symbols, word length, quantification factor, sampling rate) were systematically optimized. For Poincaré analysis, time delay was exhaustively evaluated across traditional, segmented (SPPA), and asymmetry-based descriptors, applied to all EEG representations and RR windows (30–60 s). Nonlinearity was validated using surrogate data. Linear EEG and HRV features were also assessed. Quadratic discriminant analysis with leave-one-out cross-validation was used throughout. In all cases, combining features across modalities and representations outperformed single-feature approaches.For drug discrimination, symbolic dynamics achieved the highest performance (AUC = 0.952 ND vs P; 0.919 P vs R; 0.837 ND vs R). Poincaré descriptors provided complementary results: traditional descriptors performed best for ND vs P (AUC = 0.836, F1 = 84.2%) and ND vs R (AUC = 0.835, F1 = 84.7%), while asymmetry-based descriptors were optimal for P vs R (AUC = 0.893, F1 = 90.0%).For nociception, prediction was best achieved using SPPA descriptors of RR intervals over a 30 s pre-stimulus window (AUC = 0.691, F1 = 68.3%), while detection was best with traditional Poincaré descriptors of EEG (AUC = 0.700, F1 = 72.9%).Overall, nonlinear analysis of EEG and RR signals, combined with systematic parameter optimisation, enables robust drug discrimination and meaningful nociception assessment from a single frontal EEG channel. Although nociception performance was moderate, reflecting the complexity of predicting behavioural responses under anaesthesia, the consistent benefit of multimodal and multi-representation features supports their use in next-generation personalized anaesthesia monitoring systems.
NAVARRO PONS, MARC: Smart materials and resonant microelectromechanical systems for bioelectronic microdevices and electroceuticals
Author: NAVARRO PONS, MARC
Programme: DOCTORAL DEGREE IN BIOMEDICAL ENGINEERING
Department: Department of Electronic Engineering (EEL)
Mode: Normal
Deposit date: 17/09/2026
Reading date: 06/11/2026
Reading time: 11:00
Reading place: Sala d'Actes Pepe Millán de l'Institut de Microelectrònica de Barcelona (IMB-CNM-CSIC), C. dels Til·lers, 08193, Bellaterra (Campus UAB)
Thesis director: MURILLO RODRÍGUEZ, GONZALO
Thesis abstract: This thesis presents the design, fabrication, characterization, and biological assessment of two complementary piezoelectric platforms for wireless electrical cell stimulation under ultrasound actuation: aluminum nitride (AlN)-based MEMS resonators (Part I) and electrospun silk fibroin nanofiber scaffolds and microparticles (Part II). The central question is whether ultrasound-actuated piezoelectric microdevices can generate extracellular electric fields of sufficient magnitude and spatial confinement to elicit measurable cellular responses at acoustic intensities within commonly accepted diagnostic-ultrasound exposure ranges.The AlN platform is based on circular clamped membrane resonators (~90 µm diameter) fabricated on SOI substrates using Pt electrodes. A total of 24 resonators are integrated in a 3x8 array on a 1x1 cm chip, each individually routed to its own stimulation electrode pair. The devices resonate at ~3.27 MHz in air (Q = 199) and at ~1.62 MHz immersed with one side under water (Q = 13). The AlN thin film exhibits d33,f = 1.4 pm/V, e31,f = -0.34 C/m2, and εr = 10.64, as measured by DBLI with substrate clamping correction. Impedance spectroscopy revealed a low coupling coefficient (k2eff ≈ 0.5%) and dominant parasitic capacitance from the routing traces (Cpar ≈ 8.5 pF) requiring a custom polynomial baseline subtraction method for BVD parameter extraction. The acoustic sensitivity under ultrasound actuation at 1 MHz was 11.2 mV/MPa, linear across 0.1 to 2.0 W/cm2. A COMSOL model, calibrated and compared with the experimental data, predicted electric fields above the field reported to induce Ca2+ responses due to VGCC activation in osteoblast-like cells at 0.3 W/cm2.Biological validation with Saos-2 cells established a controllable, intensity-dependent operating window: cell viability exceeded 95% at 0.1 and 0.5 W/cm2, dropping to approximately 10% at 1.0 W/cm2. This sharp transition is consistent with device-mediated electromechanical activation rather than the acoustic intensity alone. Calcium imaging at 0.3 W/cm2 showed that 20 of 34 cells (59%) on exposed electrodes exhibited calcium transients, compared with ~12% off-electrode. To our knowledge, this is the first cell-scale, resonant AlN-based microdevice for direct wireless electrical cell stimulation without external rectification or amplification circuitry.The silk fibroin trades the spatial precision and remote activation control for full biodegradability and distributed piezoelectric excitation. Electrospinning from aqueous silk fibroin solutions at (21 wt%) produced random and aligned nanofiber mats. Methanol immersion was selected among three crystallization methods for its high β-sheet content (~37-38% by FTIR amide I deconvolution, from ~27% as-spun), preserved fiber morphology, and reproducibility. Crystallization increased both stiffness and strength (E = 179 MPa, UTS = 6.1 MPa for random mats; E = 206 MPa, UTS = 11.1 MPa for aligned mats) and the piezoelectric coefficient 3.3-fold, from d33 = 2.2 pC/N to 7.3 pC/N, confirming β-sheet domains as the structural origin of the piezoelectric response. Crystallized films were converted into 130x130 µm microparticles by silicon microcutting at DTU Health Tech, preserving the nanofiber architecture.Biological validation demonstrated cytocompatibility with HaCaT, NHDF, and Saos-2 (viability >90%), proliferation kinetics comparable to glass controls, CK10 differentiation on the scaffold (~80%, comparable to glass) and cell adhesion and viability on the microparticle format.The two platforms are complementary: the AlN resonators provide controlled, individually addressable stimulation with defined frequency, intensity, spatial precision, and CMOS compatibility; the silk fibroin platform provides a biodegradable scaffold with distributed piezoelectric excitation and a microparticle format for tissue integration, where biodegradability and scaffold function take priority over electrical precision.

DOCTORAL DEGREE IN CHEMICAL PROCESS ENGINEERING

MARÍN RIVAS, ENRIQUE DAVID: Mechanochemical Preparation of Pd-Cu/CeO₂ Catalysts for Carbon Monoxide Oxidation
Author: MARÍN RIVAS, ENRIQUE DAVID
Programme: DOCTORAL DEGREE IN CHEMICAL PROCESS ENGINEERING
Department: Department of Chemical Engineering (EQ)
Mode: Normal
Deposit date: 31/07/2026
Reading date: 13/11/2026
Reading time: 11:00
Reading place: Sala Polivalent - Edifici IEEBEhttps://eebe.upc.edu/ca/lescola/com-arribar
Thesis director: LLORCA PIQUE, JORDI | VENDRELL VILLAFRUELA, XAVIER
Thesis abstract: Cerium oxide (CeO2)-based catalysts are widely studied for oxidation reactions due to their redox properties, oxygen storage capacity, and ability to stabilise metal-support interfacial sites. Among these systems, Pd/CeO2 and Cu/CeO2 catalysts are of particular interest for low-temperature CO oxidation. However, their catalytic behaviour depends on the synthesis method, metal dispersion, and defect structure. In this thesis, mechanochemical synthesis by planetary ball milling was investigated as a sustainable route for preparing CeO2-based catalysts, together with different treatments aimed at modifying their structural and catalytic properties.The first part of this work focused on Pd/CeO2 catalysts prepared by planetary ball milling under controlled atmospheres. The results demonstrated that mechanochemical synthesis promotes the formation of active Pd-Ce interfacial structures. Catalysts prepared at 250 rpm under air for 10 min exhibited the highest CO oxidation activity, outperforming those prepared by conventional impregnation. This behaviour was attributed to the formation of PdOx-Ce interfacial species and strong metal-support interactions. Electron microscopy revealed highly dispersed Pd nanoparticles and clusters on the ceria surface.The second part investigated Cu/CeO2 catalysts prepared by mechanochemical methods using different precursors, calcination temperatures, milling conditions, and reduction treatments. The results showed that mechanochemical synthesis can produce catalysts with superior performance compared with conventional methods. Catalysts prepared from copper nitrate and calcined at 300 °C showed the best activity. H2/N2 reduction further enhanced catalytic performance. Electron microscopy confirmed the absence of large CuOx aggregates and the presence of highly dispersed copper species. In addition, operando synchrotron experiments revealed dynamic Cu+/Cu2+ redox cycling under reaction conditions, contributing to catalyst stability and progressive activation.The third part addressed bimetallic Pd-Cu/CeO2 catalysts prepared by planetary ball milling. Different Cu/Pd ratios, metal loadings, and deposition sequences were evaluated. Under non-reduced conditions, Pd incorporation did not significantly improve activity compared with monometallic Cu/CeO2 catalysts. However, after H2 reduction, the bimetallic catalysts exhibited enhanced activity, highlighting the importance of pre-reduction in the formation of favourable Pd-Cu-Ce interfacial structures.Electron beam irradiation was also investigated as a post-synthesis treatment for Pd/CeO2 catalysts. Irradiation improved catalytic activity and reduced the apparent activation energy for CO oxidation. Raman and XPS analyses revealed an increase in structural defects, peroxide species, and additional PdOx-Ce interfacial sites.Finally, flash sintering was applied to ceria-based materials. Although the treatment induced significant microstructural and electrical modifications, the resulting materials exhibited lower catalytic activity than untreated CeO2, mainly due to the thermal effects associated with Joule heating.Overall, this thesis demonstrates that mechanochemical synthesis is an effective strategy for preparing highly active CeO2-based catalysts. The catalytic behaviour of Pd/CeO2, Cu/CeO2, and Pd-Cu/CeO2 systems is governed by metal-ceria interfacial structures, metal dispersion, and the redox properties of the support.
MOGHADAMFAR, TANAZ: Advanced membrane modification strategies to improve the ion selectivity of nanofiltration membranes for developing circular processing of brackish water
Author: MOGHADAMFAR, TANAZ
Programme: DOCTORAL DEGREE IN CHEMICAL PROCESS ENGINEERING
Department: Department of Chemical Engineering (EQ)
Mode: Article-based thesis
Deposit date: 22/09/2026
Reading date: 06/11/2026
Reading time: 10:30
Reading place: EEBE - UPCSala d'actes, Edifici A
Thesis director: REIG I AMAT, MÒNICA | DEL VALLE MENDOZA, LUIS JAVIER
Thesis abstract: Water treatment has become a critical global challenge due to increasing water scarcity while supporting the European Union's transition toward a circular economy and the objectives of the European Green Deal. Besides water reuse, treatment technologies are increasingly expected to recover valuable resources, including critical raw materials such as Mg²⁺, from water and wastewater streams. Membrane technology plays a central role in this transition because of its energy efficiency and high selectivity toward ionic species. Among membrane-based processes, nanofiltration (NF) has emerged as a promising technology for water treatment and selective ion separation. However, conventional NF membranes still suffer from limited ion selectivity, membrane fouling, and insufficient control of ion transport, particularly for challenging separations between monovalent and divalent ions.This thesis focused on the development of advanced NF membranes through surface modification and nanocomposite fabrication strategies to improve ion selectivity, permeability, and stability. The main approach involved layer-by-layer (LBL) assembly using poly(diallyldimethylammonium chloride)/poly(styrene sulfonate) (PDADMAC/PSS) and poly(allylamine hydrochloride)/poly(styrene sulfonate) (PAH/PSS). The influence of polyelectrolyte type, concentration, and the number of bilayers (1.5–6.5) was systematically investigated on polyamide- and sulfonated polyether sulfone-based NF membranes. A comprehensive characterization using field emission scanning electron microscopy, atomic force microscopy, Fourier transform infrared spectroscopy, Raman imaging, ellipsometry, contact angle measurements, and chemometric analysis was performed to establish relationships between membrane chemistry, morphology, multilayer formation, and separation performance.LBL modification significantly enhanced membrane selectivity and transport properties. For polyamide-based membranes, the best performance was obtained at 5.5 bilayers for PDADMAC/PSS and 6.5 bilayers for PAH/PSS, providing improved Na/Ca, Na/Mg, K/Ca, and K/Mg selectivity together with high water permeability. For sulfonated polyether sulfone membranes, lower polyelectrolyte concentrations (0.01 wt.%) produced more compact, homogeneous, and chemically stable multilayers than higher concentrations, demonstrating the importance of precise fabrication conditions.The thesis also explored thin-film composite and ultra-loose NF membranes incorporating metal-organic framework (MOF) nanoparticles. ZIF-8-NH₂ and UiO-66-NH₂ nanoparticles were synthesized and incorporated at different concentrations (0.075 and 0.15 wt.%) into the interlayer and active layer. Membrane performance strongly depended on MOF type, loading, and location within the membrane. A novel configuration combining UiO-66-NH₂ in the interlayer with ZIF-8-NH₂ in the active layer provided the best balance between permeability and ion separation.To summarize, this work demonstrates that LBL surface engineering and MOF incorporation are effective strategies for tailoring NF membrane properties. The modified membranes exhibited enhanced ion selectivity, permeability, and stability compared with commercial membranes while providing new insights into membrane structure–performance relationships. These findings contribute to the development of next-generation NF membranes for sustainable water treatment, hard water softening, and selective recovery of valuable ions, while providing a framework for future membrane design and characterization.

DOCTORAL DEGREE IN CIVIL ENGINEERING

FARSHBAF, SIMA: Computational and Data-Driven Design of Additively Manufactured Auxetic Metamaterials for Enhanced Energy Absorption
Author: FARSHBAF, SIMA
Programme: DOCTORAL DEGREE IN CIVIL ENGINEERING
Department: Barcelona School of Civil Engineering (ETSECCPB)
Mode: Article-based thesis
Deposit date: 29/09/2026
Reading date: 30/11/2026
Reading time: 11:00
Reading place: Sala Zienkiewich (CIMNE) Building C1, UPC - Campus North Gran Capitan S/N 08034 Barcelona
Thesis director: DIALAMISHABANKAREH SOLTANI, NARGES | CERVERA RUIZ, LUIS MIGUEL
Thesis abstract: This doctoral thesis presents a computational-experimental framework for the analysis, design, and optimization of auxetic metamaterials with enhanced energy absorption performance. The proposed methodology combines experimental characterization, nonlinear finite element analysis, additive manufacturing, structural design, and machine learning to enable the efficient development of lightweight metamaterials with tailored mechanical properties.The thesis first investigates the mechanical behavior of conventional and auxetic cellular structures through experimental testing and validated nonlinear finite element simulations. The analyses provide insight into the influence of cellular topology, loading direction, and deformation mechanisms on the mechanical response, demonstrating the superior energy absorption capability of re-entrant auxetic structures over conventional honeycomb architectures.Building on these findings, novel geometrical modifications of the conventional re-entrant unit cell are proposed to enhance its auxetic response and energy absorption capacity while preserving its characteristic deformation mechanisms. The numerical predictions are experimentally validated, confirming the effectiveness of the proposed designs. Compared with the base design, the modifications achieve improvements of up to 1250\% in energy absorption capacity and 860\% in specific energy absorption.To accelerate the design process, a machine learning-based forward and inverse design framework is developed using datasets generated from validated finite element simulations. The proposed methodology enables efficient prediction of nonlinear mechanical responses and inverse identification of unit-cell geometries for both uniform and thickness-graded auxetic structures with prescribed mechanical performance. Furthermore, a physics-guided machine learning framework that combines a layer-wise response decomposition strategy with deep neural networks is developed to substantially reduce the computational cost of dataset generation while enhancing predictive performance. The proposed framework achieves prediction errors below 2.5\%, including in extrapolation scenarios, using only 10 training samples, compared with 2,145 samples required by the conventional deep neural network.Overall, this thesis establishes a framework integrating computational mechanics, experimental validation, structural optimization, and physics-guided machine learning for the efficient design of advanced auxetic metamaterials. The proposed methodologies provide practical tools for developing lightweight structures with tailored mechanical performance and enhanced energy absorption, while providing a methodological basis for extending data-driven design approaches to other metamaterial systems.

DOCTORAL DEGREE IN COMPUTATIONAL AND APPLIED PHYSICS

DYHR, SØREN ISTVÁN ADORJÁN: Optimal Metrics in Odd-Dimensional Geometry: Reeb Dynamics and Applications in Fluid Mechanics
Author: DYHR, SØREN ISTVÁN ADORJÁN
Programme: DOCTORAL DEGREE IN COMPUTATIONAL AND APPLIED PHYSICS
Department: Department of Physics (FIS)
Mode: Normal
Deposit date: 11/09/2026
Reading date: 12/11/2026
Reading time: 13:00
Reading place: Aula 3.2 Edifici P
Thesis director: MIRANDA GALCERÁN, EVA | GONZÁLEZ PRIETO, ÁNGEL
Thesis abstract: Particles described by Hamiltonian mechanics tend to stay on odd-dimensional hypersurfaces of symplectic manifolds. In this thesis we study a generalization of the geometric structures appearing on such submanifolds. They all have a Reeb vector field and, in dimension three, coincide with stable Hamiltonian structures (SHS), a class which includes both cosymplectic and contact structures. We study two problems concerning metrics compatible with such structures.First we consider the Navier-Stokes equations, which describe viscous incompressible fluids. It turns out that Reeb vector fields of cosymplectic structures are stationary solutions for all compatible metrics. We create a cosymplectic plug from a construction by Cardona, Miranda, Peralta-Salas, and Presas, which realizes Turing machines as vector fields. Their construction was originally used in contact geometry to extend these vector fields to stationary solutions of the 3D Euler equations, which model ideal (inviscid) fluids. We extend their result by embedding the plug to obtain cosymplectic Reeb fields, and thus stationary solutions to the 3D Navier-Stokes equations, capable of simulating any Turing machine.Second we study an energy functional on the space of compatible metrics, originally considered for contact manifolds by Blair as well as by Chern and Hamilton. The metrics of minimal energy for contact manifolds in dimension three have been classified. We generalize the functional and the classification to three-dimensional SHSs and find that positive energy critical metrics correspond to the algebraic Anosov flows.Finally we obtain a similar result in higher dimension when there exist certain first integrals satisfying a non-degeneracy condition. We also classify a family of five-dimensional examples of critical metrics, including some not satisfying the non-degeneracy condition.
MUKHOPADHYAY, SHRODDHA: Nonlinear optics at the nanoscale: harmonic generation in resonant metasurfaces
Author: MUKHOPADHYAY, SHRODDHA
Programme: DOCTORAL DEGREE IN COMPUTATIONAL AND APPLIED PHYSICS
Department: Department of Physics (FIS)
Mode: Normal
Deposit date: 30/09/2026
Reading date: 29/10/2026
Reading time: 15:30
Reading place: Sala de conferencias, planta baja, edificio TR1, Calle Colon 1, Campus Terrassa
Thesis director: COJOCARU, CRINA MARIA | TRULL SILVESTRE, JOSE FRANCISCO
Thesis abstract: This thesis aims to contribute to two important research areas in photonics: nonlinear optics and nanophotonics (in particular metasurfaces).These interconnected fields underpin the development of next‑generation nonlinear nanophotonic devices. As modern photonic technologies increasingly rely on nanomaterials, their reduced dimensionality gives rise to new physical regimes, and we require refined theoretical tools and experimental strategies to understand and harness the behavior of sub‑wavelength structures. The work integrates concepts from nonlinear optics, condensed‑matter physics, and nanophotonics. While nonlinear optics was historically explored in bulk crystals under phase-matching conditions, recent advances in nanofabrication have shifted the focus toward nanoscale platforms where light–matter interactions are dominated by strong confinement, surface effects, and nonlocal corrections. In this regime, under ultrafast and intense light excitation, traditional bulk descriptions of nonlinear polarization are insufficient, and microscopic models become necessary. The Maxwell-hydrodynamic theoretical framework used throughout this thesis fulfills this need for a unified description. This theoretical foundation is complemented by nonlinear simulations performed using FDTD and FEM numerical methods. Experimentally, the thesis implements table-top optical setups for high-precision measurements of the efficiencies of the nonlinear harmonic generation processes, and their enhancements in the abovementioned material platforms. In this regards, we reach detection deep into the UV, a limit of far field optical measurements, as well as report two and three orders of magnitude enhancements in the harmonics generated, direct implications of resonant light localization in investigated metasurfaces.Within this framework, this thesis examines harmonic generation in two representative classes of nanomaterials: ultrathin films of semiconductors and metals, and their corresponding one‑ and two‑dimensional periodic metasurfaces. Although the geometries and fabrication strategies differ across these systems, their nonlinear optical response can be interpreted and validated using our combined experimental-theoretical methodology, which applies seamlessly across the different materials and geometries investigated. This consistent approach across disparate platforms has been a guiding principle of the work, as it enables a systematic understanding of how free and bound electrons, surface contributions, and nonlocal effects jointly shape nonlinear emission at the nanoscale.A major component of the work is the study of resonant metasurfaces in plasmonic, dielectric and hybrid-plasmonic platforms, understanding of the material properties at the nanoscale along with the strong electromagnetic field enhancement to compensate for the reduced interaction volume at the nanoscale. How these resonances arise, how they hybridize, and how they shape nonlinear emission has been central to the progression of this work. The interplay between material dispersion, geometry, and electromagnetic coupling has revealed a rich landscape of phenomena that bridge classical and emerging regimes of nanophotonics. By unifying nonlinear optical theory with nanoscale material physics, this work provides a foundation for the design and optimization of nonlinear metasurfaces, compact frequency converters, and emerging quantum photonic platforms. The results demonstrate that ultrathin films and nonlinear metasurfaces are not simply miniaturized versions of bulk materials, but fundamentally distinct systems governed by additional physical mechanisms that dominate at nanometric scales. By integrating theory, simulation, and experiment, this work provides both conceptual clarity and practical methodologies for understanding and engineering nonlinear processes in next‑generation nanophotonic devices.

DOCTORAL DEGREE IN COMPUTER ARCHITECTURE

CARO ROCA, MARTÍ: Efficient Tailoring of AI-based Solutions for Safety-Critical Systems
Author: CARO ROCA, MARTÍ
Programme: DOCTORAL DEGREE IN COMPUTER ARCHITECTURE
Department: Department of Computer Architecture (DAC)
Mode: Normal
Deposit date: 10/09/2026
Reading date: 19/11/2026
Reading time: 10:15
Reading place: C6-E101
Thesis director: ABELLA FERRER, JAIME
Thesis abstract: The increasing performance of computing platforms enables the use of larger Deep Neural Networks (DNNs) that achieve the accuracy needed for real-time applications such as Camera-based Object Detection (CBOD) in Autonomous Driving (AD). While high inference accuracy is mandatory for DNNs in Critical Real-Time Embedded Systems (CRTES), memory bandwidth and energy consumption are also critical factors, often constraining the achievable performance and determining whether those DNNs meet application requirements (e.g., frames per second). Therefore, some highly accurate but large DNN models must be optimized to operate under these constraints.Moreover, automotive applications with safety requirements must adhere to standards such as ISO 26262, which impose the use of diverse redundancy for the highest integrity level (ASIL D) to mitigate common-cause faults — random hardware faults that can potentially affect redundant components similarly and remain undetected. Dual Modular Redundancy (DMR), also referred to as Dual-Core LockStep (DCLS) for CPUs, and Triple Modular Redundancy (TMR), often combined with some form of diversity, are commonly used in safety-critical systems to provide fault detection and tolerance. However, efficiently realizing diverse redundancy for DNN-based safety-related tasks, such as object detection, remains an open challenge, as these applications can leverage semantic redundancy rather than bit-level equivalence.In this thesis, we first perform an at-scale evaluation of several approximation domains to optimize DNN models. In particular, we focus on CBOD models, which are essential in AD to detect objects surrounding the vehicle. We study several techniques independently and jointly — including reduced-precision arithmetic, approximate computing, and low-voltage operation — to assess how they can be combined to significantly reduce the area, delay, and energy of AI systems. We further extend this analysis by studying the impact of weight clustering for different arithmetic representations, including floating-point and integer formats, providing insights to guide the development of low-power CBOD accelerators with negligible impact on accuracy.Next, we design an efficient diverse redundancy scheme tailored to AI applications by leveraging reduced precision. The proposed scheme operates on reduced-precision arithmetic obtained by dropping selected bits from the fetched data of the main accelerator. This enables mitigation of random hardware faults while reducing the delay, area, and energy of the redundant accelerator compared to traditional DCLS implementations, without noticeable accuracy loss.We also propose a diverse redundancy technique with a similar cost to DCLS-like implementations but with a significant accuracy increase, achieved by mitigating AI model inaccuracies and avoiding the need for specific hardware support. This scheme applies input transformations (e.g., horizontal flip or Gaussian noise) and uses merging algorithms to fuse detections from redundant components. We further evaluate its ability to mitigate adversarial attacks, allowing a single mechanism to simultaneously address random hardware faults, DNN model inaccuracies, and adversarial attacks. Finally, we show that the scheme can be generalized to other AI models and tasks, confirming that the observed conclusions and trends hold across different AI modalities.Overall, this thesis studies approximation techniques that can significantly optimize AI models for CRTES and proposes two enhanced diverse redundancy techniques: one targeting energy efficiency and another focusing on improving the accuracy of the diverse redundant system with negligible overhead.
LEYVA SANTES, NEIEL ISRAEL: High Performance Network-on-Chip Designs for Open Manycore Systems
Author: LEYVA SANTES, NEIEL ISRAEL
Programme: DOCTORAL DEGREE IN COMPUTER ARCHITECTURE
Department: Department of Computer Architecture (DAC)
Mode: Normal
Deposit date: 16/09/2026
Reading date: 26/10/2026
Reading time: 11:00
Reading place: Sala C6-E106
Thesis director: ÁLVAREZ MARTÍ, LLUC | VALLEJO GUTIÉRREZ, ENRIQUE
Thesis abstract: The transition from multicore to manycore architectures is a direct consequence of the limitations of frequency scaling and the need to sustain performance growth through parallelism. As a result, modern processors integrate an increasing number of cores within a single chip, which significantly raises the demands on on-chip communication and memory systems.In parallel, open instruction set architectures such as RISC-V have enabled the emergence of open hardware ecosystems, where full-system platforms can be designed and explored at the Register-Transfer Level (RTL). Over time, open manycore platforms have evolved from simple educational designs into more complete and scalable infrastructures, incorporating coherent memory hierarchies and Network-on-Chip (NoC) interconnects. This evolution has positioned them as promising candidates not only for reproducible architectural research, but also as realistic foundations for the design and evaluation of future high-performance computing systems. However, reaching this level of applicability requires further improvements in scalability and efficiency, particularly in communication and memory subsystems.In this context, this thesis focuses on advancing the maturity of an open manycore platform to support both architectural research and the development of high-performance computing systems. The objective is to provide a flexible and scalable infrastructure that enables detailed evaluation at the RTL while remaining representative of realistic HPC workloads and supporting system design. To achieve this, the thesis focuses on improving the communication and memory subsystems, as they play a key role in determining system scalability and overall performance. The proposed approach combines methodological and architectural contributions to reduce evaluation cost, enhance system performance, and extend platform configurability. The thesis is structured around three main contributions.First, it presents SynFull-RTL, a methodology for evaluating RTL NoC designs using traffic models derived from real application behavior. By leveraging probability-weighted sampling of execution macro-phases, this approach significantly reduces simulation time while preserving representativeness, achieving up to 40x reduction in time-to-solution.Second, the thesis introduces OpenPiton4HPC, a set of architectural extensions that improve memory bandwidth, reduce communication latency, and increase system configurability. These enhancements include support for multiple memory controllers, NoC optimizations such as router bypassing and wider links, and a tunable cache hierarchy, leading to performance improvements of up to 7.2x over the baseline.Finally, the thesis proposes GS-NoC, a hardware mechanism designed to improve communication efficiency for irregular memory access patterns in vector workloads. By coalescing memory requests at different levels of the system, GS-NoC reduces the number of NoC packets by 52% on average with respect to the Bypass configuration and achieves a geometric mean speed-up of 1.18x compared to a baseline NoC configuration.All contributions are implemented at synthesizable RTL and validated through cycle-accurate simulation, ensuring realistic evaluation and practical applicability. Overall, this work advances the design of open manycore platforms, improving their scalability and effectiveness as research and development infrastructures for future computing systems.
NIU, YUYAO: Hardware-Aware Algorithms for Efficient Graph Traversal on High-End Parallel Architectures
Author: NIU, YUYAO
Programme: DOCTORAL DEGREE IN COMPUTER ARCHITECTURE
Department: Department of Computer Architecture (DAC)
Mode: Normal
Deposit date: 09/09/2026
Reading date: 06/11/2026
Reading time: 11:00
Reading place: Sala d'Actes B6
Thesis director: CASAS GUIX, MARC
Thesis abstract: Graph traversal is a fundamental building block in scientific computing, data analytics, and artificial intelligence. Among traversal methods, Breadth First Search (BFS) and Depth First Search (DFS) are two core primitives for higher-level graph algorithms. As graph-structured data continues to grow, efficient traversal increasingly depends on the ability to exploit high-end parallel architectures. However, modern architectures rely on structured and throughput-oriented execution engines, such as long vector processors, dense matrix accelerators, and massively parallel GPUs, whereas graph traversal exhibits irregular memory accesses, data-dependent control flow, and dynamic workloads.This dissertation argues that the performance gap between graph traversal and modern hardware arises from three major mismatches: a structural mismatch between regular execution units and irregular graph structure, a computational mismatch between arithmetic-oriented acceleration and relation-driven traversal, and an architectural mismatch between throughput-oriented parallelism and dependency-driven traversal behavior. To address these challenges, the dissertation develops a hardware-aware design paradigm in which graph traversal algorithms are reformulated according to the characteristics and constraints of the target architecture.The first contribution is ALBBA, an algebraic BFS algorithm for long vector architectures. ALBBA reformulates direction-optimized BFS using a vector-friendly sparse representation, vectorized search, and a two-level bypass mechanism. These techniques allow the key algebraic BFS operations, sparse matrix-vector multiplication (SpMV) and sparse matrix-sparse vector multiplication (SpMSpV), to better exploit long vector execution while reducing unnecessary memory accesses and redundant computation. This shows that structural irregularity can be mitigated through hardware-aware data layout and execution design.The second contribution is BerryBees, a BFS framework for Tensor Core-based GPUs. BerryBees introduces the Binarized Row Slice format, which uses bit-level representations to support both SpMV and SpMSpV within a unified data layout while reducing memory overhead. It further employs a warp-level algorithm that leverages Tensor Core matrix-multiply-accumulate instructions to accelerate traversal. This shows that relation-driven graph computation can be reorganized to exploit hardware originally designed for dense matrix operations.The third contribution is DiggerBees, a parallel DFS algorithm for massively parallel GPUs. DiggerBees introduces a two-level stack structure aligned with the GPU memory hierarchy, warp-level DFS execution within thread blocks, and hierarchical work stealing across warps and blocks. These techniques expose scalable parallelism for dependency-driven traversal and improve load balance across the GPU. This shows that DFS, despite its inherent dependencies, can be effectively adapted to throughput-oriented parallel hardware.Together, these contributions demonstrate that BFS and DFS can be effectively accelerated when their data representations, computational formulations, and execution structures are designed with architectural constraints as first-class considerations. More broadly, this dissertation establishes hardware-aware reformulation as a general methodology for rethinking irregular graph algorithms on increasingly specialized parallel hardware.

DOCTORAL DEGREE IN COMPUTING

PONS RECASENS, GERARD: Evolving Knowledge Graphs: Continuous Knowledge Acquisition, Adaptation, and Exploitation
Author: PONS RECASENS, GERARD
Programme: DOCTORAL DEGREE IN COMPUTING
Department: Department of Computer Science (CS)
Mode: Normal
Deposit date: 01/10/2026
Reading date: 11/11/2026
Reading time: 15:00
Reading place: Sala d'Actes FIB - Edifici B6 Campus Nord
Thesis director: QUERALT CALAFAT, ANNA | BILALLI, BESIM
Thesis abstract: Modern systems continuously generate vast amounts of heterogeneous data. While this data contains valuable information, its full potential remains difficult to realize when it is treated as a collection of isolated observations lacking meaning and context. Unlocking its full value therefore requires enriching data with explicit semantics that capture concepts, relationships, and contextual information, transforming isolated observations into structured knowledge. This thesis argues that such semantic enrichment constitutes a fundamental step towards building more effective intelligent systems. In this context, Knowledge Graphs (KGs) provide a natural framework for representing and organizing this knowledge, while Knowledge Graph Embeddings (KGEs) transform it into latent representations that can be flexibly exploited by modern Machine Learning methods to address evolving analytical needs.As knowledge is not static, realizing this vision requires addressing challenges that span its entire life cycle. First, data originating from heterogeneous and unstructured sources must be incorporated into a continuously evolving knowledge structure, where it is enriched with semantics. Second, as this knowledge structure evolves, its latent representations must be continuously updated while preserving previously acquired knowledge. Finally, the evolving knowledge structure and its learned representations must be effectively exploited to support adaptive behavior in intelligent systems.This thesis addresses these challenges through the framework of evolving KGs. For knowledge acquisition from unstructured sources, it focuses on textual information and proposes a schema-aware entity disambiguation method that combines Large Language Models (LLMs) with the hierarchical structure of KGs, improving adaptability across domains without requiring model retraining. To maintain latent representations over time, it investigates the continual adaptation of KGEs. Within this scope, it identifies a previously overlooked phenomenon that contributes to the degradation of previously acquired knowledge and proposes an adjusted evaluation protocol to accurately capture its effects. Additionally, it introduces an initialization strategy for newly added embeddings that enhances knowledge acquisition while mitigating this performance degradation. Finally, for knowledge exploitation, it demonstrates how continuously evolving KGs and their learned representations can support adaptive recommendation and intent-aware assistance in experiment-driven data analytics environments. Together, these advancements contribute towards enabling evolving KGs to continuously acquire, adapt, and exploit knowledge.

DOCTORAL DEGREE IN CONSTRUCTION ENGINEERING

BAJIC, PETAR: Assessment of structural performance of fibre-reinforced concrete under chloride-induced corrosion
Author: BAJIC, PETAR
Programme: DOCTORAL DEGREE IN CONSTRUCTION ENGINEERING
Department: Department of Civil and Environmental Engineering (DECA)
Mode: Normal
Deposit date: 17/09/2026
Reading date: 23/10/2026
Reading time: 11:00
Reading place: CAMPUS NORD - Carrer de Jordi Girona, Les Corts, 08034 Barcelona - Edifici B1-003
Thesis director: DE LA FUENTE ANTEQUERA, ALBERTO | TOSIC, NIKOLA
Thesis abstract: Chloride-induced corrosion is a major contributor to the degradation of steel-based products (i.e., bars, fibres, and others) used to reinforcing concrete structures. Namely, chlorides, if present in sufficient concentration, degrade the protective oxide layer that forms around the steel-based products in an alkaline environment, such as that existing in concrete. In the ensuing electrochemical reaction, the steel cross-section is reduced and consequently structural performance (in terms of load-bearing capacity and ductility) is degraded. It is well established that cracking, which normally occurs during service in reinforced concrete elements under bending, detrimentally affects durability and, by extension, overall structural performance. Conversely, the addition of fibres to a concrete matrix is widely recognized to enhance post-cracking performance by effectively arresting macro-crack growth. Consequently, investigating the extent to which fibre reinforcement can mitigate durability degradation constitutes a vital advancement in structural engineering research.With the goal of assessing the structural performance of different concrete types (with and without using fibres in combination with traditional rebar), in this study, a comprehensive experimental program was performed. In the experimental matrix three different concrete types were considered: conventional (rebar-only) reinforced concrete (RC) and two variants of rebar-reinforced fibre-reinforced concrete (RR-FRC). Specifically, rebar-reinforced steel-fibre-reinforced concrete (RR-SFRC) and rebar-reinforced polypropylene-fibre-reinforced concrete (RR-PPFRC). The test matrix consisted of: three distinct loading conditions (non-loaded, unloaded and elements under sustained flexural loads), two cracking states (cracked and non-cracked elements) and two exposure conditions (elements exposed to a 3% sodium chloride solution and non-exposed elements). The research was conducted both on small-scale elements (in laboratory conditions) and large-scale elements (in-situ conditions) The results indicate comparable corrosion kinetics across all considered concrete types, characterized by similar periods of corrosion initiation and propagation. However, despite fibres functioning as an invariant parameter relative to corrosion kinetics, the RR-FRC elements exhibited superior structural performance relative to the traditional RC elements. In certain instances, the mechanical performance of corroded RR-FRC elements surpassed that of the non-corroded RC controls. Moreover, the RR-FRC elements demonstrated superior behaviour under long-term sustained loading; they exhibited the capacity to recover a significant portion of their deformation upon unloading, whereas the conventional RC elements sustained permanent, irreversible deformation.
BASAR, IMREN: Sustainable Repair Mortars for Historic Masonry: Design, Characterization and Performance Assessment of Natural and Recycled Materials
Author: BASAR, IMREN
Programme: DOCTORAL DEGREE IN CONSTRUCTION ENGINEERING
Department: Department of Civil and Environmental Engineering (DECA)
Mode: Normal
Deposit date: 29/09/2026
Reading date: 10/11/2026
Reading time: 12:00
Reading place: ETSECCPB, Sala C2-212 (C/Jordi Girona 1-3, mòdul C2, Campus Nord, Barcelona).
Thesis director: FERNANDEZ CARRASCO, LUCIA | TORRENS MARTÍN, DAVID
Thesis abstract: This thesis presents a comparative and conservation-oriented investigation of lime-based mortars employed in architectural heritage, with particular emphasis on material compatibility, durability, and long-term performance. Historical mortars sampled from two representative sites in Catalonia: the Roman archaeological site of Altafulla and the Edifici de l’Hivernacle in Barcelona, were studied alongside commercial restoration mortars and laboratory-designed lime-based formulations in order to evaluate technological choices, performance limits, and conservation suitability.A comprehensive multi-analytical methodology was adopted, integrating mineralogical, granulometric, mechanical, physical, and visual characterization techniques. This approach enabled the assessment of binder–aggregate relationships, microstructural features, mechanical response, and aesthetic behaviour, with special attention to parameters relevant to heritage conservation practice. The characterization of historical mortars revealed distinct technological strategies in raw material selection, aggregate grading, and binder composition, closely linked to the functional requirements and environmental exposure conditions of each site.Comparative analysis demonstrated that standardized commercial repair mortars, despite exhibiting higher mechanical strength, do not necessarily ensure improved compatibility with historic masonry substrates. In contrast, laboratory-prepared lime mortars, particularly CL90 air-lime-based systems, showed greater adaptability in balancing mechanical performance and deformability when curing conditions and environmental exposure were carefully controlled.Durability-related conditioning tests highlighted the critical influence of carbonation regime, moisture interaction, salt presence, and thermal cycling on mortar behaviour over time, underscoring the necessity of long-term evaluation beyond early-age mechanical performance. In situ qualitative observations conducted at the Monestir de Sant Joan les Fonts further confirmed satisfactory workability, adhesion, and visual integration of both reference and PDMS-modified lime mortars during the initial post-application period.Taken together, the results support an integrated and context-sensitive approach to the design and assessment of compatible lime-based repair mortars, providing experimental evidence that emphasizes the importance of aesthetic or integration considerations, material compatibility, durability, and environmental interaction in the conservation of architectural heritage.Keywords: Lime-based mortars, heritage conservation, material compatibility, durability, long-term performance, carbonation, CL90 air lime, repair mortars, microstructure, environmental exposure, thermal cycling, salt interaction, aesthetic considerations, architectural heritage.

DOCTORAL DEGREE IN EARTHQUAKE ENGINEERING AND STRUCTURAL DYNAMICS

CUESTA ARRILLAGA, JOKIN: Physics-integrated artificial intelligence prognostics for wind turbine components under uncertainty and data scarcity
Author: CUESTA ARRILLAGA, JOKIN
Programme: DOCTORAL DEGREE IN EARTHQUAKE ENGINEERING AND STRUCTURAL DYNAMICS
Department: Department of Civil and Environmental Engineering (DECA)
Mode: Normal
Deposit date: 11/09/2026
Reading date: 20/11/2026
Reading time: 10:00
Reading place: EEBE. CAMPUS BESOSEdifici ASala PolivalentAvinguda d'Eduard Maristany, 16, 08019 Barcelona / Sant Adrià de Besòs
Thesis director: VIDAL SEGUI, YOLANDA | LETURIONDO ZUBIZARRETA, URKO | POZO MONTERO, FRANCESC
Thesis abstract: Wind energy plays a central role in the global transition towards sustainable energy systems. However, the operation and maintenance costs associated with wind turbines remain a major challenge, particularly due to unexpected component failures and the difficulty of accurately predicting their remaining useful life. Prognostics and health management has emerged as a key enabler of predictive maintenance strategies, yet current prognostic approaches often face limitations related to data scarcity and quality, limited observability of degradation, lack of physical consistency, and inadequate uncertainty quantification.This thesis addresses these challenges by developing adaptive physics-informed prognostic methodologies for wind turbine components operating under uncertainty and with scarce degradation data. A unified uncertainty-aware prognostic framework is proposed that integrates physical knowledge into data-driven models based on the observability level of the degradation process. For directly observable degradation scenarios, physics-informed neural network architectures based on time-series transformers are developed by embedding degradation laws into the learning process. For indirectly observable degradation scenarios, where degradation cannot be measured directly, a physics-guided framework is introduced to construct health indicators from operational data through residual-based representations of healthy system behavior.The proposed methodologies are validated through a two-level experimental strategy. First, a dedicated laboratory testbed is designed to generate controlled, run-to-failure wear data. Second, the framework is assessed using real wind turbine SCADA data from gearbox degradation cases to demonstrate its applicability in operational environments. In both scenarios, multiple uncertainty quantification techniques are investigated and benchmarked to characterize predictive confidence and support maintenance decision-making.The results demonstrate that integrating physical knowledge into advanced deep learning architectures improves degradation forecasting accuracy, robustness, and remaining useful life estimation performance compared with purely data-driven approaches. Furthermore, the proposed uncertainty-aware framework provides reliable confidence estimates, enhancing the interpretability and operational usefulness of prognostic predictions. The findings contribute to the development of trustworthy hybrid prognostic systems for wind energy applications and maintenance timing, supporting wind-farm availability management and the dependable operation of renewable energy assets.
WANG, SHUN: Artificial Intelligence for Wind Turbine Condition Monitoring and Fault Diagnosis Towards Predictive Maintenance
Author: WANG, SHUN
Programme: DOCTORAL DEGREE IN EARTHQUAKE ENGINEERING AND STRUCTURAL DYNAMICS
Department: Department of Civil and Environmental Engineering (DECA)
Mode: Article-based thesis
Deposit date: 21/09/2026
Reading date: 23/11/2026
Reading time: 10:00
Reading place: EEBE. CAMPUS BESOSEdifici ASala PolivalentAvinguda d'Eduard Maristany, 16, 08019 Barcelona / Sant Adrià de Besòs
Thesis director: POZO MONTERO, FRANCESC | VIDAL SEGUI, YOLANDA
Thesis abstract: The continued expansion of wind energy depends on the reliable operation of large, complex, and geographically dispersed turbines. Late fault detection can cause secondary damage, unplanned downtime, and costly maintenance. Artificial intelligence can learn condition patterns from turbine measurements, but its practical use is limited by scarce fault labels, changing operating conditions, noise, and interactions among variables.This doctoral thesis develops and validates physically informed artificial intelligence strategies for wind turbine predictive maintenance through three information routes: vibration monitoring, supervisory control and data acquisition (SCADA) monitoring, and thermal-image fault diagnosis. The research combines unsupervised representation learning, healthy-data-based anomaly detection, normal behavior modeling, and machine-learning classification. Vibration and SCADA data are used to detect departures from healthy operation, whereas thermal images distinguish labeled electrical equipment conditions. The central question is how representations, references, and inputs can preserve health-related information and support reliable detection or diagnosis. The vibration research progresses from component-level degradation assessment to system-level monitoring and robustness under variable speed. Across four public bearing run-to-failure datasets, the logarithmic envelope spectrum is combined with a variational autoencoder trained only on healthy data to produce an interpretable health index for early fault detection and degradation tracking. For vibration data from an operational research wind turbine, a variational autoencoder learns healthy feature distributions, while exponentially weighted moving average monitoring stabilizes the reconstruction-error indicator for detecting documented pitch faults, icing events, and aerodynamic imbalances. In controlled gearbox experiments, a speed-invariant autoencoder integrates order-domain alignment, cross-condition interpolation, and speed-conditioned reconstruction to obtain stable and threshold-consistent health indicators across the evaluated steady, variable, and unseen speeds. The SCADA-based research examines how input design affects the early-fault sensitivity of normal behavior models. Conventional models can conceal early degradation when highly correlated internal inputs carry the same fault influence as the monitored target. An exogenous-variable virtual sensor is therefore constructed using ambient temperature and wind speed, enabling a model to learn normal drivetrain thermal behavior from inputs external to component health. Across three gearbox- and generator-bearing fault cases from turbines of different manufacturers, the method provides warnings up to 93 days before maintenance and no false alarms during separately assessed healthy periods. The thermal-image research develops a non-contact diagnostic framework for electrical equipment. Modified multiscale two-dimensional color distribution entropy is introduced as a compact feature set extracted directly from pseudocolor thermograms before machine learning classification. In the evaluated induction-motor and power-transformer experiments, diagnostic accuracy exceeds 95\%, and the method performs better under the tested noise and limited-sample conditions than the compared grayscale entropy formulations.Within the evaluated datasets and experimental conditions, the vibration studies demonstrate early degradation assessment and system-level fault detection; the SCADA study provides earlier warnings for the documented drivetrain bearing cases; and the thermal-image study improves fault classification for the evaluated electrical equipment. Together, these results provide proof of principle for earlier and more informative condition assessment while identifying the fleet-level and field validation required before routine integration into wind turbine maintenance decisions.

DOCTORAL DEGREE IN ELECTRICAL ENGINEERING

ALI, SALMAN: ANALYSIS OF THE INTEGRATION OF RENEWABLE ENERGY AND ENERGY STORAGE SYSTEMS IN SERIES-CASCADED MICROGRIDS
Author: ALI, SALMAN
Programme: DOCTORAL DEGREE IN ELECTRICAL ENGINEERING
Department: Department of Electrical Engineering (DEE)
Mode: Normal
Deposit date: 29/09/2026
Reading date: 05/11/2026
Reading time: 10:00
Reading place: ESEIAAT – TR5 – Expo Sala
Thesis director: BOGARRA RODRIGUEZ, SANTIAGO | CORCOLES LOPEZ, FELIPE
Thesis abstract: The increasing integration of renewable energy sources has transformed power distribution networks into decentralized energy systems. Distributed generation (DG) sources, including solar photovoltaic (PV), wind, fuel cells, hydropower, and other renewable resources, enable localized power generation and reduce dependence on centralized infrastructure. Microgrids (MGs) have emerged as a solution for integrating DG units. MG structures are widely adopted due to their control and protection characteristics. However, the low-voltage output of DG sources requires boost conversion stages, increasing system complexity and losses. Consequently, series-cascaded microgrids (SCMGs) have attracted attention.Despite their advantages, SCMGs face challenges in power sharing, voltage regulation, and integration of non-dispatchable DG (ND-DG) sources. Existing power routing controls generally assume ideal DC sources, limiting their applicability under renewable intermittency. Conventional approaches such as fundamental voltage reconstruction and third-harmonic voltage injection exhibit limited power sharing capability, constrained DC-link voltage margins, overmodulation risks, and degraded dynamic performance. To address these limitations, this thesis investigates an SCMG topology integrating ND-DG units and battery storage systems. A multi-power-carriers based control strategy incorporates third-harmonic current injection alongside fundamental component to enable inter-module power routing under reduced or zero ND-DG output. The proposed approach maintains desired multilevel output voltage while satisfying voltage and modulation constraints. In addition, proportional DC-side power utilization coordinates battery charging and discharging, supporting voltage stability and power sharing. Although the proposed methodology extends the power routing range, injected third-harmonic current appears in the grid current, affecting grid compatibility.To overcome this limitation, the SCMG architecture is extended to a delta-connected configuration. In this topology, third-harmonic current circulates within cascaded clusters, enabling inter-module power routing without propagating harmonics to the grid. Zero-sequence current injection is employed for inter-cluster power routing and symmetrical three-phase currents. The proposed framework determines optimal third-harmonic current, third-harmonic voltage, and zero-sequence injection, together with proportional DC-side power utilization control. Under adverse operating conditions, the fundamental component governs grid-compliant power exchange, the third-harmonic component enhances inter-module power redistribution, and the zero-sequence component ensures inter-cluster balancing.The proposed methodology is validated through MATLAB/Simulink simulations, hardware-in-the-loop experiments, and a laboratory-scale prototype. The results confirm stable multilevel voltage generation, effective DC-link stabilization, and robust inter-module and inter-cluster power routing. Overall, this thesis advances the state-of-the-art in SCMGs by introducing a scalable control framework that enables reliable operation under realistic microgrid conditions and supports renewable energy and energy storage integration in future smart power systems.
CABRERA REDONDO, ÓSCAR: From Optimisation to Artificial Intelligence Solutions for Modern Power Systems
Author: CABRERA REDONDO, ÓSCAR
Programme: DOCTORAL DEGREE IN ELECTRICAL ENGINEERING
Department: Department of Electrical Engineering (DEE)
Mode: Normal
Deposit date: 30/09/2026
Reading date: 29/10/2026
Reading time: 10:00
Reading place: ETSEIB - UPC
Thesis director: ARAGÜÉS PEÑALBA, MÒNICA
Thesis abstract: Modern power systems are being transformed by decarbonisation, renewable generation, distributed energy resources, electrification, digitalisation, and the participation of new agents. These changes increase operational complexity and require faster, adaptive, and distributed decision-making tools. In this context, optimisation provides the structure to enforce physical and operational constraints, while artificial intelligence contributes prediction, approximation, adaptation, and privacy-preserving learning capabilities.This thesis investigates the transition from optimisation-based formulations towards artificial-intelligence-assisted solutions for modern power systems through three contributions at grid, community, and user levels. At the grid level, a physics-informed graph-based Lagrangian neural network is proposed for direct current optimal power flow approximation. By combining a graph neural network with an augmented Lagrangian loss, the model improves physical consistency under limited training. The approach is evaluated on benchmark electrical networks from 3 to 118 buses, including normal and stressed variants. Under normal conditions, the mean equality-constraint residual is reduced from 0.0219 p.u. to 0.0137 p.u., corresponding to a 37.44% improvement over a purely data-driven graph neural network, while achieving average speed-ups of 94 and 15 times over a widely used nonlinear optimisation solver under normal and extreme conditions, respectively.At the community level, the thesis analyses the smart operation of local energy communities under organisational and operational constraints. The results show that collective self-consumption can create substantial aggregate value, but that user composition and allocation rules strongly influence benefit distribution. In the evaluated case studies, configurations with 50% of users owning private photovoltaic generation reach the highest average total savings, 97.42€, while surplus reallocation increases total savings by up to 45.14€. However, fairness-aware mechanisms remain necessary to reduce negative individual outcomes.At the user level, the thesis proposes personalised federated learning strategies for household electricity demand forecasting. The proposed model-based clustering approaches improve personalisation without centralising private smart meter data. Compared with conventional federated learning, FedEC and FedKM reduce the mean squared error by 0.27% and 0.67%, respectively, with FedKM achieving the lowest aggregate error among the personalised methods considered.Overall, this thesis shows that optimisation and artificial intelligence are complementary components of modern power systems. Their responsible integration requires methods that are accurate, efficient, physics-aware, regulation-aware, and user-centred.
DIAZ MOTTA, ARMANDO: Large Scale Wind-Powered Offshore Ammonia Production
Author: DIAZ MOTTA, ARMANDO
Programme: DOCTORAL DEGREE IN ELECTRICAL ENGINEERING
Department: Department of Electrical Engineering (DEE)
Mode: Normal
Deposit date: 30/07/2026
Reading date: 29/10/2026
Reading time: 12:00
Reading place: Aula 28.8 Sala Conferències - ETSEIB-UPC
Thesis director: DÍAZ GONZÁLEZ, FRANCISCO | VILLA ARRIETA, MANUEL RICARDO
Thesis abstract: Hydrogen and renewable ammonia can support the decarbonization of sectors where direct electrification is not feasible, with green ammonia serving as a hydrogen carrier between renewable production areas and distant demand centers.Among the large-scale pathways proposed in recent years, green ammonia production based on renewable electricity and water electrolysis has attracted increasing attention. However, limited attention has been given to the long-term feasibility of off-grid offshore ammonia production based on SOEC electrolysis and powered entirely by offshore wind. This thesis specifically addresses this knowledge gap by investigating whether large-scale, off-grid offshore green ammonia production based on solid oxide electrolysis (SOEC), a Haber-Bosch synthesis loop (HB), and offshore wind power can become a feasible pathway by 2050.This research aims to assess the technical and long-term economic feasibility of offshore wind-based ammonia production. To this end, the thesis combines a state-of-the-art assessment with a thermodynamic steady-state model of a 3 000 tpd SOEC-Haber-Bosch ammonia plant and an integrated pseudo-dynamic framework that couples the plant, offshore wind generation, battery storage, and hydrogen buffer storage. This framework includes an economic model and a capacity optimization algorithm to minimize the levelized cost of ammonia (LCOA). The pseudo-dynamic model simulates flexible plant operation over an extended load range under site-specific wind conditions. Three offshore locations with high-quality wind resources, namely La Guajira (Colombia), the North Sea (Denmark), and the Gulf of Roses (Spain), are assessed in this thesis. The results show that, at steady state and full load, the plant requires 1 030 MW and reaches an overall efficiency of 63% on an LHV-NH3 basis, while the SOEC electrolyzer achieves 87% on an LHV-H2 basis. The Haber-Bosch loop supplies 57% of the thermal power required for feedwater evaporation in the electrolysis process. Under partial-load operation, the SOEC unit reaches minimum loads of 61% and 72% for steam conversion scenarios of 70% and 85%, respectively, with stack temperature variations within ±30 °C. Under offshore operating conditions, the overall efficiency reaches 60%, and the plant capacity factor reaches 80% at the best-performing site, about 10% below that of a conventional grid-connected ammonia plant.Regarding economic feasibility, the results indicate that La Guajira and the North Sea could achieve the lowest levelized cost of ammonia by 2050, at 472 and 436 USD t-1, respectively, whereas the Gulf of Roses remains less competitive at around 720 USD t-1.A comparison across the selected sites further shows that inflexible Haber-Bosch operation, i.e., operation at 100% load, results in only marginal production cost differences relative to flexible operation over the 10-100% load range, but increases Haber-Bosch hot-standby events, revealing a trade-off between operational stability and storage requirements.The thesis provides a techno-economic assessment framework for offshore green ammonia production, while also supporting the capacity planning of offshore renewable resources. Furthermore, the results indicate that SOEC-based offshore ammonia could become cost-competitive by 2050, provided that costs decline for floating wind and solid oxide electrolysis, and that exogenous factors such as supportive market conditions and favorable policy frameworks emerge.

DOCTORAL DEGREE IN ELECTRONIC ENGINEERING

HUSSAIN, TAUSEEF: Metamaterial-inspired Flexible Microwave Resonant Structures for RFID Sensing and Shielding
Author: HUSSAIN, TAUSEEF
Programme: DOCTORAL DEGREE IN ELECTRONIC ENGINEERING
Department: Department of Electronic Engineering (EEL)
Mode: Article-based thesis
Deposit date: 20/07/2026
Reading date: 05/11/2026
Reading time: 15:30
Reading place: Sala de Conferències TR1 – ESEIAAT, UPChttps://meet.google.com/fkh-mqus-zbt
Thesis director: GIL GALI, IGNACIO | FERNANDEZ GARCIA, RAUL
Thesis abstract: Metamaterials are artificially engineered electromagnetic structures that enable wave–matter interactions beyond those achievable with natural materials. When implemented in flexible and textile-compatible forms, metamaterial-inspired resonant structures offer unique advantages for wearable technologies, including conformability, lightweight construction, and tunable electromagnetic responses. These characteristics make them particularly attractive for wearable radio-frequency identification (RFID) sensing platforms, where sensitivity, robustness, and user safety must be addressed simultaneously. This thesis investigates the design and integration of metamaterial-inspired flexible resonant structures with RFID systems to enable sensors with enhanced performance and improved electromagnetic safety.Conventional RFID-based sensors typically rely on antenna deformation or substrate perturbation alone to transduce physical or biochemical stimuli. While effective in controlled environments, these approaches exhibit limited sensitivity and strong dependence on environmental loading in wearable scenarios. At ultra-high-frequency (UHF) bands, proximity to the human body causes impedance detuning and reduced read range reliability, whereas at microwave frequencies, increased absorption in biological tissue raises specific absorption rate (SAR) concerns. These limitations motivate the use of engineered resonant structures that offer enhanced field confinement and improved control over antenna–environment interactions.This thesis first investigates resonator-assisted passive UHF RFID sensing for biomechanical and biochemical monitoring. Electromagnetic coupling between printed UHF RFID tag antennas and compact resonant elements is exploited to enhance sensitivity beyond conventional antenna-based sensing. Meander-line resonators are integrated with RFID tags to enable displacement sensing, demonstrating reliable respiration and knee-flexion monitoring. The coupling approach is further extended to biochemical sensing through the integration of a capacitively coupled complementary split-ring resonator (CSRR) with an H-slot RFID antenna, enabling passive wireless monitoring of glucose concentration via read-range modulation.Subsequently, the thesis addresses electromagnetic stability and user safety at microwave RFID frequencies, where shorter wavelengths enable compact resonator arrays to be realized as metasurfaces. A breathable ground-grid artificial magnetic conductor (GG-AMC) is developed to mitigate body-induced detuning, suppress backward radiation, and significantly reduce SAR while preserving wearer comfort. In addition, near-field coupled metamaterial absorbers are introduced as dual-function structures that simultaneously provide electromagnetic shielding and strain-sensitive resonance modulation, enabling respiration monitoring at microwave frequencies. The core scientific contributions of this research are documented in five JCR-indexed journal articles and two peer-reviewed IEEE conference publications, and the thesis is presented as a compendium of articles.Keywords – RFID sensing, wearable sensors, microwave resonators, metasurfaces, biomechanical monitoring, biochemical sensing, artificial magnetic conductor, metamaterial absorber, electromagnetic shielding.
SOLÉ LLOVERAS, JORDI: Methods for Assessing Radiated Electromagnetic Emissions In Situ. Application to Photovoltaic Systems
Author: SOLÉ LLOVERAS, JORDI
Programme: DOCTORAL DEGREE IN ELECTRONIC ENGINEERING
Department: Department of Electronic Engineering (EEL)
Mode: Normal
Deposit date: 17/09/2026
Reading date: 28/10/2026
Reading time: 10:30
Reading place: Aula de Graus C4-002, campus nord UPC
Thesis director: AZPÚRUA AUYANET, MARCO AURELIO | SILVA MARTINEZ, FERNANDO
Thesis abstract: The increasing deployment of large electrical systems, renewable energy plants and power electronic installations is creating new challenges for electromagnetic compatibility assessment. Many of these systems cannot be tested in conventional EMC laboratories because they are too large, too powerful, too distributed or too dependent on their final installation conditions. As a result, radiated electromagnetic emissions must often be assessed in situ, under real operating conditions and in uncontrolled electromagnetic environments. In these scenarios, the measurement problem is no longer limited to comparing a spectrum with a limit.This thesis develops and validates methods for assessing radiated electromagnetic emissions in situ, with photovoltaic systems as the main application case. The work proposes a structured measurement methodology covering the complete assessment process, from planning and characterization of the electromagnetic environment to the selection of measurement axes and points, emissions assessment and interpretation of results. Ambient noise is addressed as one of the most frequent and difficult challenges in practical in situ radiated emission assessments. Since the measured spectrum may contain contributions from both the equipment under test and external electromagnetic sources, the interpretation process must distinguish emissions generated by the installation from those associated with the surrounding electromagnetic environment. To support this interpretation, several complementary strategies are experimentally validated, including closer distance measurements, conducted RF current measurements, reduced RBW analysis, amplitude probability distribution analysis, etc.The thesis also addresses one of the most critical open issues in practical in situ assessments: the application of radiated emission limits when the measurement distance differs from the reference distance defined by the standard. It is shown that the conversion should be applied to the emission limit rather than to the measured field level, avoiding an unintended relaxation of the permissible level. Specific H-field distance conversion factors below 30 MHz are derived from analytical equations, numerical simulations and experimental validation, and are simplified into practical expressions and tables suitable for direct use in measurement procedures and standardization documents. For E-field emissions above 30 MHz, the classical inverse distance relation is justified and its limitations are quantified.The thesis results are applied to three real photovoltaic installations with different characteristics and measurement constraints. These campaigns demonstrate the applicability of the proposed methodology under real operating conditions and show the practical difficulties associated with in situ radiated emission assessment. The results show that robust conclusions require the use of complementary techniques to determine if an installation is prone to generate interferences.Beyond the experimental validation, the thesis has been transferred to scientific publications, industrial EMC assessment activities and standardization work. The proposed methodology, the ambient noise interpretation and the H-field distance conversion factors have been presented and discussed in technical committees involved in the development of future EMC standards. Therefore, this thesis provides not only an academic and scientific contribution, but also a practical technical reference for engineers, stakeholders and technical committees involved in the application and future development of EMC standards.
VAQUERO GALLARDO, NOELIA: Contribution to Advanced Electrical Sensing and Predictive Control for Enhanced Safety in Cryolipolysis Applications
Author: VAQUERO GALLARDO, NOELIA
Programme: DOCTORAL DEGREE IN ELECTRONIC ENGINEERING
Department: Department of Electronic Engineering (EEL)
Mode: Article-based thesis
Deposit date: 01/10/2026
Reading date: 05/11/2026
Reading time: 11:00
Reading place: Aula A0.02, Edifici
Thesis director: MARTINEZ GARCIA, HERMINIO | MILLÁN BLASCO, OLIVER
Thesis abstract: Active medical devices based on energy application, such as radiofrequency, cryotherapy, or electrosurgery, are widely used due to their therapeutic effectiveness in non-invasive treatments. However, one of their main side effects is the occurrence of thermal injuries and skin burns, associated with inadequate energy delivery or insufficient monitoring of tissue status. In clinical practice, many of these devices do not integrate advanced sensing and control techniques, operating with fixed parameters regardless of patient-specific characteristics, which leads to significant variability in therapeutic outcomes. Therefore, monitoring mechanisms are required to assess tissue status and dynamically adjust treatment parameters, ensuring both efficacy and safety.This Ph.D. Thesis addresses this challenge through a multidisciplinary approach based on fractional electrical modeling, early detection of tissue freezing, and adaptive process control derived from the electrical and dielectric variations of biological tissues. First, a modeling framework based on fractional-order systems is established to describe the electrical behaviour of technical coatings and biological tissues. Using Cole-family models fitted to experimental impedance data, it is demonstrated that fractional models accurately reproduce the complex electrical response while using a reduced number of parameters, thereby simplifying instrumentation and facilitating the miniaturization of embedded systems. Building on this framework, the use of bioimpedance and capacitance is explored as tools for the early detection of tissue freezing, providing alternatives to methods based solely on temperature. Bioimpedance enables freezing detection by measuring the increase in resistance and the decrease in conductance that occur when water within the tissue turns into ice, whereas capacitance reflects the reduction in dielectric constant associated with ice crystal formation, thereby identifying early changes in water structure. Finally, a predictive model based on Random Forest is developed, integrating bioimpedance, capacitance, and temperature signals to anticipate freezing events.The results of this Ph.D. Thesis show that predictive detection occurs, on average, 16 seconds earlier than capacitive detection and 63 seconds earlier than temperature–bioimpedance-based detection. This model enables the implementation of a real-time adaptive control strategy over the cooling system, reinforcing operational safety. In summary, this Ph.D. Thesis demonstrates that the electrical properties of tissues allow freezing events to be identified before conventional temperature indicators, establishing a clinically relevant framework for intelligent, non-invasive cryotherapy devices that improve efficacy, safety, and regulatory compliance.

DOCTORAL DEGREE IN ENGINEERING, SCIENCES AND TECHNOLOGY EDUCATION

FREIXANET DE LA IGLESIA, MARIA JOSEP: Teaching statistics through environmental inquiries: the problematisation process
Author: FREIXANET DE LA IGLESIA, MARIA JOSEP
Programme: DOCTORAL DEGREE IN ENGINEERING, SCIENCES AND TECHNOLOGY EDUCATION
Department: Institute of Education Sciences (ICE)
Mode: Normal
Deposit date: 29/09/2026
Reading date: 26/10/2026
Reading time: 17:00
Reading place: Sala d'Actes FIB (híbrida amb GMeet)
Thesis director: ALSINA AUBACH, MONTSERRAT | BOSCH CASABÒ, MARIANNA
Thesis abstract: Against the background of the growing role of data and of socially relevant, real-world problems in statistics education, this doctoral thesis investigates the teaching and learning of statistics through inquiry-based instructional formats at the secondary and university levels, with a specific focus on the problematisation process: the transition from a broad, vague generating question to precise statistical investigative questions and, from these, to the design of data collection. The research is framed within the Anthropological Theory of the Didactic (ATD) and the proposal of study and research paths (SRPs) to facilitate the change of educational paradigm, from visiting works to questioning the world.The thesis is articulated through three studies. The first analyses the shift from traditional Project-Based Learning (PBL) to the SRPs proposal and its consequences in the reorganisation of the responsibilities (topogenesis) of teachers and students, while also examining its effects on students' perceptions and engagement. The second study addresses the problem of the teacher management of the problematisation process at the secondary school level, distinguishing the passage from the generating question to the investigative question and the passage from the investigative question to the data-collection instrument. The third study moves to the tertiary level, where it examines how the problematisation process unfolds with real-world sustainability data, differentiating two ways of starting the inquiry: when data are provided or when data needs to be found to answer a question. It also identifies the teaching practices and instructional devices that teachers mobilise to sustain the inquiry.The studies draw on five empirical implementations organised around environmental and sustainability themes. Three were carried out at the secondary level — an initial project-based activity on noise pollution, a first SRP on waste separation, and a collaborative cross-year SRP on air pollution — while two took place within a first-year statistics course for engineering students, addressing water and the building's electricity consumption using real institutional datasets.Across educational levels, the results converge in showing that problematisation is a teachable but non-spontaneous competence that must be explicitly designed and engineered, and that the shift of responsibility from teacher to students depends on enabling conditions such as a legitimising external organisation, a deliberately insufficient initial milieu, structured moments for classifying and refining questions, and the progressive enrichment of the milieu. It also identifies constraints mostly originated by the persistence of the paradigm of visiting works and the lack of adequate pedagogical and didactic infrastructures to sustain the management of inquiry processes. A central, transversal finding is an initial characterisation of teaching praxeologies that help manage different types of problematisation processes and, beyond them, the inquiry dialectics, paying particular attention to the shift of responsibilities between teachers and students. In doing so, the thesis offers a concrete didactic account, within the ATD, of how the responsibility for posing and refining statistically studiable questions can be transferred to students, contributing to current efforts to connect statistics learning with authentic, socially and environmentally relevant inquiry and with the development of students' civic awareness.

DOCTORAL DEGREE IN ENVIRONMENTAL ENGINEERING

GARATACHEA SOLÉ, ROGER: Domestic and Transboundary Air Pollution across European countries: health impacts and policy implications
Author: GARATACHEA SOLÉ, ROGER
Programme: DOCTORAL DEGREE IN ENVIRONMENTAL ENGINEERING
Department: Department of Civil and Environmental Engineering (DECA)
Mode: Normal
Deposit date: 07/09/2026
Reading date: 28/10/2026
Reading time: 11:00
Reading place: UPC, Campus NordBuilding C1. Classroom: 002C/Jordi Girona, 1-308034 Barcelona
Thesis director: PEREZ GARCIA-PANDO, CARLOS | JORBA CASELLAS, ORIOL
Thesis abstract: Air pollution remains a major environmental and public health problem in Europe, despite decades of regulation and substantial improvements in air quality. Fine particulate matter (PM2.5) and tropospheric ozone (O3) remain two of the most harmful air pollutants. Their formation, transport, and impacts are shaped by complex interactions between local emissions, transboundary pollution, natural sources, atmospheric chemistry, and meteorology. This complexity makes it difficult to determine which sources are responsible for observed pollution levels.This thesis studies the geographical origins of O3 and both geographical and sectoral origins of PM2.5 across Europe, evaluating their implications for health impacts and air-quality governance. Using the CALIOPE regional air-quality modeling system, a tagging approach is applied to quantify domestic, transboundary, maritime, and hemispheric contributions to O3 concentrations and associated short-term mortality. For PM2.5, a perturbation-based approach combined with an observation-constrained methodology is used to quantify source contributions and their related short-term mortality impacts, which are further disaggregated by economic sector and natural sources. The thesis also assesses the feasibility of achieving the revised European Ambient Air Quality Directive PM2.5 standards by 2030 under both current legislation (CLE) and maximum feasible reduction (MFR) pathways.The findings indicate that European air pollution is strongly shaped by sources beyond the jurisdiction in which exposure occurs. For O3, hemispheric and transboundary contributions represent major components of surface concentrations across Europe, underscoring the importance of long-range transport. Consequently, the health impacts related to O3 are also largely driven by imported pollution, which accounts for 88% of all O3-attributable deaths in Europe. However, during high-ozone episodes, local and regional contributions become more relevant, suggesting that domestic and nearby emission reductions can be particularly important under peak O3 levels. For PM2.5, short-term mortality impacts are also strongly influenced by cross-border transport and by the sectoral composition of emissions. Anthropogenic emissions accounted for 55% of PM2.5-attributable deaths, with 35% of this anthropogenic burden resulting from cross-border transport within Europe. Residential combustion emerges as the main anthropogenic source, although agriculture, road transport, industry, and power generation can also make important contributions depending on the region and season.The PM2.5 compliance analysis shows that projected emission reductions are expected to substantially improve air quality by 2030, but important governance challenges will remain. CLE would enable many European air-quality zones to meet the revised PM2.5 limit values, with further improvements under MFR. However, a large share of zones reaching compliance under CLE reductions remain cooperation-dependent, requiring emission reductions in foreign countries rather than domestic action alone. Excluding natural-source contributions further increases attainment, highlighting the role of sources that are not fully controllable through national mitigation policies. These results show that remaining non-compliance reflects not only insufficient domestic action, but also transboundary dependencies, natural contributions, and the limitations of national policy instruments.Overall, this thesis demonstrates that source-resolved modelling is crucial to understand air quality results, estimate health impacts, and design effective compliance strategies. Achieving European air-quality standards will require not only ambitious emission reductions, but also coordinated international action and regulatory frameworks that explicitly account for the transboundary nature of atmospheric pollution.
OBRADOR REY, SERGI: Physics-based state estimation algorithms for battery management systems
Author: OBRADOR REY, SERGI
Programme: DOCTORAL DEGREE IN ENVIRONMENTAL ENGINEERING
Department: Department of Civil and Environmental Engineering (DECA)
Mode: Normal
Deposit date: 18/09/2026
Reading date: 29/10/2026
Reading time: 11:00
Reading place: CAMPUS BESOSSala Polivalent de l'Edifici A.EEBEAv. Eduard Maristany, 16
Thesis director: TRILLA ROMERO, LLUÍS | CANALS CASALS, LLUC
Committee:
     PRESIDENT: OYARBIDE URQUIZU, MIKEL
     SECRETARI: AMANTE GARCIA, BEATRIZ
     VOCAL: AUGUST JOHANSSON, SVEN AAKE
Thesis abstract: For real sustainability, especially in the electric mobility sector, the trend of oversizing batteries and moving the computational capabilities of management systems to the cloud needs to be reduced. Such practices are harmful to the alignment of these systems with the eco-design principles that these technologies should follow, thus dramatically increasing the resources needed to produce and operate them. The real solution is to maximize the life and efficiency of the resources we do have.As a first step toward gaining better control, the state estimation algorithms of the cells within the battery must be improved. Hence, equipping the management system with capabilities derived directly from battery physics and electrochemistry is presented as the key concept of this thesis. Instead of states being estimated through externally processed empirical data, the deployment of advanced physics-based models directly onto the battery's embedded electronics is proposed. By this means, the internal dynamics and aging of the cells can be understood, predicted, and adapted in real-time with enough accuracy.This allows them to take control of their own operations and avoid early internal decay. The research undertaken in this thesis finally demonstrates that battery systems can be converted into more durable, efficient energy assets that are more in line with the principles of eco-efficiency only by directly integrating electrochemical knowledge into the battery’s embedded electronics.

DOCTORAL DEGREE IN GEOTECHNICAL ENGINEERING

GUO, TIAN: Reducing the uncertainty on subsurface heterogeneity from microseismicity and geodetic data
Author: GUO, TIAN
Programme: DOCTORAL DEGREE IN GEOTECHNICAL ENGINEERING
Department: Department of Civil and Environmental Engineering (DECA)
Mode: Normal
Deposit date: 01/10/2026
Reading date: 24/11/2026
Reading time: 15:00
Reading place: ETSECCPB.UPC, Campus NordBuilding C1. Classroom: 002C/Jordi Girona, 1-308034 Barcelona
Thesis director: VILARRASA RIAÑO, VÍCTOR | WU, HAIQING
Thesis abstract: Ensuring geological CO₂ storage (GCS) at scale requires more than just the availability of pore space. Thermal, hydraulic, mechanical, and chemical interactions during and after CO₂ injection pose challenges due to limited access to the subsurface. Reducing subsurface uncertainty is one of the most critical challenges for the safe deployment of GCS. This thesis aims to reduce this uncertainty by linking ground deformation and induced microseismicity with data-driven inversion, stress analysis, and numerical hydromechanical modeling. Specifically, this thesis evaluates (1) how ground deformation can be predicted and used to infer subsurface and fault properties, (2) how microseismic focal mechanisms can constrain the stress state and fault reactivation, and (3) how coupled simulators can support the long-term interpretation of the hydromechanical responses of Opalinus Clay to the injection of CO₂-rich brine.First, we develop a multi-stage, data-driven workflow to predict ground deformation caused by fluid injection into fault-intersected reservoirs. Synthetic poroelastic solutions are used for training and verified against CODE_BRIGHT simulations. Deep neural networks outperform linear regression and tree-ensemble-based models. Pressure change, shear modulus, and fault geometry are dominant controls on surface displacement, enabling rapid assessment of subsurface conditions. Secondly, we propose a probabilistic inversion framework based on conditional invertible neural networks (cINNs) to estimate subsurface parameters from geodetic data and quantify the non-uniqueness of the characterization. Synthetic tests demonstrate that the reservoir width is well-constrained and the throw-to-thickness ratio is partially constrained, whereas the fault dip is only weakly identifiable due to its subtle surface expression. Application of this framework to the Illinois Basin-Decatur Project (IBDP) reveals an equivalent reservoir width that increases over time, consistent with the lateral migration of the pressurized region.We also constrain the stress state and fault stability at the IBDP by integrating microseismic focal mechanisms with borehole data, vertical stress estimates, and instantaneous shut-in pressure (ISIP) measurements. Slip-tendency analysis of 26 focal mechanisms suggests a maximum horizontal stress (SHmax) magnitude of approximately 93 MPa, oriented at ~110°. Fault reactivation thresholds prove to be highly sensitive to ISIP, SHmax magnitude, and the fault friction coefficient.Finally, we present hydromechanical benchmark simulations based on the CO₂LPIE experiment at the Mont Terri Underground Rock Laboratory in Switzerland, comparing CODE_BRIGHT and OpenGeoSys in axisymmetric, anisotropic, and stratified configurations. Both codes reproduce consistent patterns of pressure diffusion and dissolved CO₂ transport, whereas the geomechanical fields (deformation, displacement, and stress) exhibit strong sensitivity to bedding anisotropy and constitutive models, highlighting the need for careful calibration against field data.Overall, this thesis demonstrates that GCS safety benefits from addressing monitoring, modeling, and uncertainty quantification in an integrated manner. Ground deformation provides spatial information on reservoir pressurization, microseismicity constrains stresses and fault reactivation, and machine learning models enable rapid prediction and probabilistic inversion for subsurface characterization. The proposed framework improves the characterization of subsurface structures and properties while supporting an adaptive assessment of GCS safety.

DOCTORAL DEGREE IN MARINE SCIENCES

MURILLO GOMEZ, NELSON ENRIQUE: COASTAL HAZARDS, RISKS AND ADAPTATION IN THE BARRA DE SALAMANCA (COLOMBIA)
Author: MURILLO GOMEZ, NELSON ENRIQUE
Programme: DOCTORAL DEGREE IN MARINE SCIENCES
Department: Department of Civil and Environmental Engineering (DECA)
Mode: Normal
Deposit date: 16/07/2026
Reading date: 23/10/2026
Reading time: 11:00
Reading place: Place: ETSECCPBUPC, Campus NordBuilding C2. Classroom: 212C/Jordi Girona, 1-308034 Barcelona
Thesis director: JIMENEZ QUINTANA, JOSE ANTONIO
Thesis abstract: Coastal sandbars are generally elongated landforms that provide protection to environmental, socioeconomic, and infrastructural ecosystems from extreme events, but are highly sensitive to morphodynamic changes driven by natural and anthropogenic processes. This is further complicated by the uncertainty Surrounding the expected sea-level rise in the coming years. These dynamics generate pressures and motivate land managers to understand the genesis of their morphological evolution, the physical agents that modulate it, the risks associated with the diversity of their exposed ecosystems within a tolerance limit, and to propose sustainable adaptation measures for the future.This research reconstructs the morphological evolution of the Salamanca coastal sandbar (Colombia), located within a national and international conservation park, over the last 40 years, the physical agents present at decadal and episodic scales, and the assessment of threats associated with erosion and flooding. Understanding the past and considering sea level rise projections to 2050, a projected area susceptible to damage was identified. Within this area, the exposure and vulnerability of the exposed elements were assessed, and this, when integrated with the threats, allowed for the estimation of environmental, physical, and socioeconomic risks. Recognizing that risk cannot be eliminated because it is intrinsic to coastal development, we have proposed a model of tolerable risk levels and a list of adaptation measures grouped into strategies according to their implications for the coastal zone. The results showed that the sandbar is decreasing its sediment transport rates, indicating a search for morphological equilibrium. However, erosion rates of around -20 m/a at the most critical points are deteriorating environmental services. This, combined with storm impacts and the increasing construction of hard infrastructure, has worsened and increased the exposure of the main land-based infrastructure connecting the region's most important towns with the interior of the country. This is putting commercial and tourism activities at risk. The communities located in these areas are in highly vulnerable socioeconomic conditions, generating pressure on the coastline that requires urgent intervention. The tolerable risk for environmental, physical infrastructure, and socioeconomic factors was established according to the degree of impact on the exposed element, its loss of natural value, and the resulting repercussions on the quality of life of the inhabitants. This criterion facilitates a balance of sustainable development between the inhabitants and their environment. The risk reduction strategies demonstrated that acting alone does not achieve tolerable risks, and only a comprehensive strategy allows us to approach the proposed objectives. Prompt action is needed to reduce uncertainty and increase objectivity about the expected future. We conclude that the methodology developed allowed us to understand what has happened, why, under what conditions the threats have evolved, the morphological evolution, the risk typologies, what could happen, and how to reduce the probability of the expected changes. We believe this methodological process can be replicated and adapted to any coastal area with the specific characteristics required. keywords: morphology, risks, adaptation measures.

DOCTORAL DEGREE IN NETWORK ENGINEERING

GONZALEZ DIAZ, NESTOR: Improving the Scalability and Availability of Indoor Location Solutions based on Wi-Fi RTT (IEEE 802.11mc)
Author: GONZALEZ DIAZ, NESTOR
Programme: DOCTORAL DEGREE IN NETWORK ENGINEERING
Department: Department of Network Engineering (ENTEL)
Mode: Normal
Deposit date: 24/09/2026
Reading date: 02/12/2026
Reading time: 11:00
Reading place: Sala de actos (001-Pg) de la EETAC (Castelldefels)
Thesis director: ZOLA, ENRICA VALERIA | MARTÍN ESCALONA, ISRAEL
Thesis abstract: Indoor positioning is a key enabler of location-based services, yet no technology has emerged as the indoor counterpart of Global Navigation Satellite Systems (GNSS). Wi-Fi Round-Trip Time (RTT), standardized in IEEE 802.11mc through the Fine Timing Measurement (FTM) protocol, is a promising candidate because it reuses the Wi-Fi infrastructure already deployed in most buildings and measures distances with sub-meter error on commodity devices.However, the real-world adoption of Wi-Fi RTT is hindered by three interrelated challenges. Accuracy degrades under Non-Line-of-Sight (NLoS) and multipath conditions, which introduce systematic biases in the ranging estimates. Scalability is capped by the peer-to-peer nature of the FTM protocol, which requires an exchange of location-dedicated frames between the user and the access point (AP). This procedure limits each AP to about thirty users updating their position once per second. Finally, the machine-learning models required to exploit the signal often exceed the memory and the latency budget of resource-constrained devices. This dissertation argues that accuracy, scalability, and computational complexity must be optimized jointly, meeting the memory, latency, and availability requirements of Internet of Things (IoT) deployments.The system proposed in this dissertation is based on fingerprinting and combines the active RTT observable with passive observables that add no network overhead, namely the Received Signal Strength Indicator (RSSI) and the standard deviation of the RTT burst. The system is organized into a clustering stage, a feature selection stage, and a multiclass classification stage. All experiments use measurement campaigns collected with commodity smartphones in two real scenarios: an open auditorium, with line of sight (Line-of-Sight, LoS) between users and APs, covered by up to seven 802.11mc-compatible APs, and a floor of corridors and offices, without line of sight (NLoS), served by three APs.The dissertation first characterizes the accuracy achievable with Wi-Fi RTT fingerprinting. Two RTT requests attain very good results in both scenarios: an accuracy above 97% in LoS when one RSSI is added, and a root-mean-square error (RMSE) between 50 and 65 cm in NLoS when three RSSI are added. It then proposes a clustering and feature selection architecture that distributes the FTM traffic overhead among the APs, increasing the potential number of concurrent users without degrading accuracy. Third, a low-complexity proximity model, which replaces the iterative clustering and the tree-based feature selection with a single sort of the observed RSSI, matches the accuracy of more complex algorithms while reducing the clustering memory to less than half and storing no persistent model; in addition, the RMSE stays below 30 cm in LoS and near half a meter in NLoS. Finally, it reveals that the RSSI from passive scanning and the RSSI from the FTM frames are systematically biased and locally uncorrelated observables that carry complementary positional information. The proposed system, evaluated with both sources, reaches an RMSE of 0.66 m at the highest user count and of 0.30 m with the three RTT requests in NLoS.Taken together, these results show that accuracy, scalability, and low computational complexity can be achieved jointly on commodity hardware, providing an integrated and reproducible framework for scalable and available indoor localization based on Wi-Fi RTT.
MIRANDA PASCUAL, ÀLEX: Toward a Theory of Preprocessing and Composition in Differential Privacy
Author: MIRANDA PASCUAL, ÀLEX
Programme: DOCTORAL DEGREE IN NETWORK ENGINEERING
Department: Department of Network Engineering (ENTEL)
Mode: Change of supervisor
Deposit date: 24/09/2026
Reading date: 28/01/2027
Reading time: 11:00
Reading place: Karlsruhe
Thesis director: PARRA ARNAU, JAVIER | STRUFE, THORSTEN
Thesis abstract: Personal data processing is essential to many applications from health to finance and urban development, but data may contain sensitive information, raising serious privacy concerns. Privacy-enhancing technologies therefore seek to preserve utility while limiting disclosure, creating an inherent privacy–utility tradeoff. Differential privacy (DP) has become the standard privacy notion due to its strong protection guarantees and rigorous definition. Its composition properties allow complex DP mechanisms from simpler ones, with post- and preprocessing techniques extending DP to more tasks. However, DP can struggle to achieve an acceptable privacy–utility tradeoff for complex tasks. Moreover, the central DP model requires trust in a single curator, whereas local approaches remove this assumption at a substantial utility cost. Thus, this thesis studies how the main building blocks of DP—composition and preprocessing—affect the privacy–utility tradeoff and how this tradeoff adapts to the trust requirements. Understanding these tools helps researchers develop better DP mechanisms.We study the privacy-preserving publication of human trajectory databases under DP and syntactic notions. Our systematization shows that DP suffers under the increased complexity and presence of outliers. Because these limitations are not exclusive to trajectories, we focus the remainder of this thesis in general contexts.The negative impact of outliers motivates our study on record omission in DP as preprocessing. We first analyze sampling that amplifies privacy by uniformly selecting a subset of records of the database. However, our evaluation shows that core DP mechanisms with sampling consistently yield a worse tradeoff than the mechanism without sampling over all tested scenarios. We then introduce suppression in DP and derive theorems characterizing the privacy guarantees under arbitrary suppression, showing that it can either amplify or degrade privacy depending on the setting. However, targeted outlier suppression also does not improve the privacy–utility tradeoff and is less effective than sampling. These results show that preprocessing should be evaluated based on its overall privacy–utility impact, rather than privacy amplification alone.We next study composition and its interaction with preprocessing. Existing sequential and parallel composition theorems may overestimate privacy loss when mechanisms access overlapping data, while parallel composition does not apply to some DP variants, including bounded DP. We therefore introduce a general composition theorem that accounts for deterministic preprocessing under arbitrary domains and neighborhood definitions. By modeling data access as preprocessing, our theorems provide tighter privacy estimates and enable intermediate composition settings between sequential and parallel composition.Finally, we investigate limiting data access to reduce trust requirements. We introduce the k-curators model, positioned between the central and local models, where disjoint portions of the data are randomly distributed among k non-colluding curators. To analyze this model, we introduce probabilistic parallel composition, which provides tighter privacy bounds than standard parallel composition. Our evaluation of the privacy–utility–trust tradeoff shows that the k-curators model can limit the data accessed by each entity with minimal utility cost at fixed privacy levels.Overall, this thesis advances the understanding of DP by characterizing the effects of composition, preprocessing, and their interaction. It provides new theoretical results, more accurate privacy guarantees, and evaluates their impact on the privacy–utility(–trust) tradeoff, offering new insights and tools for designing future DP mechanisms.

DOCTORAL DEGREE IN PHOTONICS

MINI DAS, APARNA: Investigation of Dynamic Catalyst -Electrolyte Interfaces in Electrochemical Systems for Establishing Activity Correlations.
Author: MINI DAS, APARNA
Programme: DOCTORAL DEGREE IN PHOTONICS
Department: Institute of Photonic Sciences (ICFO)
Mode: Normal
Deposit date: 28/09/2026
Reading date: pending
Reading time: pending
Reading place: pending
Thesis director: GARCÍA DE ARQUER, FRANCISCO PELAYO
Thesis abstract: Rising atmospheric CO2 levels, driven primarily by anthropogenic emissions, are a central contributor to global climate change and environmental instability. Electrocatalytic reactions provide a promising pathway for CO2 emission reduction through electricity-driven conversion of CO2 and/or water into value-added chemicals and fuels. In this context, catalysts are widely recognized as the key determinants of performance. This thesis takes the discussion a step further by examining how, in specific reactions, not only the catalyst itself but also the surrounding environment becomes crucial in defining performance matrices. More specifically, it investigates dynamic catalyst-electrolyte interfaces in water electrolysis and direct bicarbonate reduction, with a particular emphasis on interfacial dynamics and their correlation with activity.The thesis first outlines the fundamental aspects of water electrolysis and bicarbonate reduction, and then presents new concepts and findings on the role of dynamic catalyst-electrolyte interfaces in these systems. To do so, several in situ and operando techniques are employed, including Raman spectroscopy, X-ray absorption spectroscopy, electrochemical impedance spectroscopy, and quantitative bubble analysis, complemented by other electrochemical methods to establish correlations with performance.In the case of water electrolysis, the thesis focus on the oxygen evolution reaction, which represents the largest energetic penalty in the system. Correlations spanning atomic to microscale features are identified, collectively shaping the performance landscape of the reaction. Using complementary in situ probes, these correlations are shown to go beyond simple cause-and-effect relationships, highlighting the importance of uncovering the underlying mechanisms rather than relying solely on performance metrics.In direct bicarbonate reduction, performance is strongly influenced by the availability of CO2 at the interface, which is in turn linked to the local pH. Through operando Raman spectroscopy, a descriptor for the interfacial buffering state is identified, providing indirect insight into instantaneous CO2 availability. This hypothesis is further validated by engineering the catalyst-electrolyte interface to actively tune this descriptor, resulting in a threefold improvement in performance. This demonstrates how mechanistic understanding of the catalyst-electrolyte interface can guide informed hypotheses that are ultimately deterministic, reducing the experimental parameter space and improving optimization efficiency.Overall, the thesis emphasizes the importance of understanding why performance improves, rather than simply attributing progress to “better catalysts”. A deeper understanding of these systems, especially at high current densities, can lead to more coherent design principles and challenge existing assumptions about major factors governing the electrochemical performance. The results show that the catalyst surface cannot be considered a static entity, but rather a dynamic system whose structure and reactivity are continuously modulated by external factors. These insights provide a more detailed mechanistic picture of electrochemical interfaces and help identify key descriptors for catalyst design.
NI CHUIREAIN, FIONNUALA: Intrinsic Randomness in Quantum Information Theory
Author: NI CHUIREAIN, FIONNUALA
Programme: DOCTORAL DEGREE IN PHOTONICS
Department: Institute of Photonic Sciences (ICFO)
Mode: Normal
Deposit date: 24/09/2026
Reading date: 30/10/2026
Reading time: 10:00
Reading place: ICFO Auditorium
Thesis director: ACÍN DAL MASCHIO, ANTONIO
Thesis abstract: The unpredictability of quantum physics gives rise to an intrinsic form of randomness. We generate it by performing a measurement on a quantum state. In an adversarial scenario, we then quantify the randomness by the probability that a correlated eavesdropper could correctly guess the measurement outcomes. This thesis addresses the following question: given a known quantum state and measurement, how much intrinsic randomness can we generate?.We begin by solving the maximal intrinsic randomness of any state, optimised over all projective measurements, for four figures of merit: the conditional min, max and von Neumann entropies, and the newly introduced unambiguous randomness. The last of these describes an eavesdropper who is never wrong, but can sometimes return an inconclusive outcome. Relaxing the assumption of perfect accuracy, we also consider randomness given a fixed rate of inconclusive outcomes. We solve both of these quantities for any state and projective measurement in dimension two. If the setup is used to choose random bases for a prepare-and-measure quantum key distribution protocol, we show that the unambiguous randomness captures the knowledge gained by an eavesdropper about the secret key, without causing any disturbance. We then turn to extremal measurements beyond the projective case, which may have more outcomes. An unbiased measurement is one whose outcomes all have the same a priori probability. We characterise the randomness generated by any unbiased extremal rank-one measurement acting on any state, solving the problem explicitly in dimension two. Four-outcome measurements of this type are tomographic, such that one can fully determine the state from the outcome probabilities, so these results hold for fully source-device-dependent randomness too. The tetrahedral symmetric informationally complete (SIC) measurement, we find, has the least intrinsic randomness within this class. We also present the biased skewed SIC family of measurements, and use them to prove that 2 log d bits of randomness, the maximal amount, can be generated device-dependently (or source-device-independently) in any dimension d in which there exists a SIC measurement.Reversing the scenario, we optimise over all states to solve the maximal intrinsic randomness of two classes of non-extremal, or noisy, measurements: any two-outcome measurement in dimension two, and projective measurements in any dimension affected by isotropic noise. In a realistic implementation, however, neither the state nor the measurement is completely free of noise. Taking a simple example of a state measured in a basis of any dimension, we compare the case where only one device is affected by noise to that where both devices are noisy, such that an eavesdropper may have joint correlations with the state and the measurement. For a fixed amount of total noise, we find that, contrary to the common practice of attributing all experimental noise to a single device, the joint-noise eavesdropper strictly outperforms her single-noise counterpart, compromising the security of the randomness generated.That the outcomes of a quantum measurement are unpredictable has been known since the inception of quantum theory. This thesis presents a step forward in analytically characterising the intrinsic randomness of quantum physics. On the practical side, our results bound the functionality of quantum random number generators, under various assumptions. On a more foundational level, this work provides a new lens through which to compare quantum states and measurements and, from their vast landscape, to see which of them emerge as the most random.

DOCTORAL DEGREE IN SIGNAL THEORY AND COMMUNICATIONS

NAVARRO GESSÉ, EDGAR: Synthesis, Design and COM-based Modeling of SAW Electroacoustic Filters
Author: NAVARRO GESSÉ, EDGAR
Programme: DOCTORAL DEGREE IN SIGNAL THEORY AND COMMUNICATIONS
Department: Department of Signal Theory and Communications (TSC)
Mode: Normal
Deposit date: 01/10/2026
Reading date: 02/11/2026
Reading time: 11:00
Reading place: Aula C4-028-2, Edifici C4 de l'Escola d'Enginyeria de Telecomunicació i Aeroespacial de Castelldefels (EETAC) de la UPC
Thesis director: COLLADO GOMEZ, JUAN CARLOS | MATEU MATEU, JORDI
Thesis abstract: Radio-frequency filtering is a key function in modern wireless systems, where demanding requirements on selectivity, bandwidth, compactness, and integration must be satisfied within a limited physical footprint. Surface acoustic wave (SAW) devices provide an attractive solution because they combine strong frequency selectivity with compact implementation. However, as filtering functions become more complex and conventional ladder architectures are complemented by multimode and hybrid configurations, the design problem extends beyond the synthesis of an equivalent electrical network: synthesized resonances, couplings, capacitances, and impedance levels must ultimately be translated into a physically realizable acoustic structure subject to the electroacoustic coupling, dispersive behaviour, geometrical constraints, and fabrication limitations of the selected technology.This thesis addresses the absence of a sufficiently systematic bridge between a prescribed filtering function and the final geometry of advanced SAW filters. The central approach is a synthesis–technology-adaptation–Coupling-of-Modes (COM) framework in which network synthesis establishes the required resonant and coupling conditions, topology-dependent circuit transformations or resynthesis enforce physical compatibility, and the resulting targets are mapped onto distributed COM structures that account for the physical dimensions of the interdigital acoustic geometry. The objective is not to replace physical modelling with an ideal equivalent circuit, but to preserve an explicit and repeatable correspondence between the prescribed response, a technology-compatible circuit representation, and the final acoustic geometry.At synthesis level, a unified driving-point extraction procedure combines finite-transmission-zero nonresonating-node sections with transmission-zero-at-infinity resonating-node sections, allowing a single filtering function to be partitioned between independent ladder resonators and multimode coupled-resonator-filter (CRF) sections. An enforced-zero deflation technique improves the numerical robustness of high-order synthesis by restoring the exact prescribed immittance root before each polynomial division. For conventional ladder filters, the Butterworth–Van Dyke-to-COM mapping is extended to a synthesis–mapping–resynthesis loop that accounts for the dispersive electroacoustic coupling of the technology.For multimode structures, a circuital Nakamura COM formulation and inverse-design procedures are developed for isolated two- and three-mode CRFs and extended to mixed ladder–CRF and cascaded-CRF architectures. The resulting design flow combines topology-specific circuit transformations with COM-based inverse design, incorporating the required impedance scalings, inverter transformations, static-capacitance compensation, technology-aware resynthesis, and physical partitioning needed to obtain realizable structures and ultimately determine their geometric dimensions. The proposed synthesis and circuit-transformation procedures are formulated and implemented as systematic and repeatable numerical procedures, while CRF inverse design and COM evaluation are implemented in a dedicated MATLAB design tool. The methodology is validated through COM simulations and measurements of fabricated ladder and two-mode CRF devices. A complementary tunable in-line filter further demonstrates the broader applicability of synthesis-based electroacoustic filter design, with measured continuous centre-frequency tuning from 0.99 to 1.03 GHz and up to 100% bandwidth control.Overall, the thesis establishes a systematic but topology-dependent methodology for translating prescribed filtering functions into physically realizable electroacoustic filter geometries, extending synthesis-driven design from conventional SAW ladders to multimode, mixed, cascaded, and reconfigurable architectures.

DOCTORAL DEGREE IN SUSTAINABILITY

GOUMEIDA, KENZA: Consumer and Citizen Preferences Towards Sustainability Attributes and Practices
Author: GOUMEIDA, KENZA
Programme: DOCTORAL DEGREE IN SUSTAINABILITY
Department: University Research Institute for Sustainability Science and Technology (IS.UPC)
Mode: Normal
Deposit date: 28/09/2026
Reading date: pending
Reading time: pending
Reading place: pending
Thesis director: GIL ROIG, JOSE MARIA | RAHMANI MEDDOUR, DJAMEL
Thesis abstract: The transition towards sustainable food systems is widely recognized as one of the major challenges of our time, spanning the production, processing, marketing, and consumption of food. While research on the social, environmental, and economic dimensions of this shift is extensive on the supply side, less is known about the demand side: how consumers and citizens value and act upon sustainability attributes embedded in food products and dietary practices. This thesis addresses this gap by examining consumer and citizen preferences towards sustainability attributes and practices at three points of the food value chain: production, product/distribution, and consumption. Three specific objectives follow: i) to estimate citizens' willingness to support the environmental, social, and economic benefits of more sustainable pig production; ii) to examine consumers' valuation of sustainable, biodegradable food packaging and how it changes as information grows, identifying the underlying consumer segments; and iii) to identify the latent drivers and segments of adherence to a healthy, sustainability-relevant dietary pattern across Spain, Morocco, and Turkey, together with their explanatory factors.For the production objective, a discrete choice experiment (DCE) was administered via an online survey and analyzed with a conditional logit model (CLM) to estimate willingness to pay (WTP) for ecosystem service attributes. Results show significant citizen support for a shift towards lower crude-protein pig diets that reduce environmental emissions and soybean use while improving animal welfare. Support is highest for the greatest environmental improvement and differs across sociodemographic groups, particularly by gender and rural/peri-urban residence, indicating that support for upstream production changes is real but conditional rather than universal.For the product/distribution objective, a non-hypothetical, incentive-compatible experimental auction (EA) was conducted face-to-face using a sequential information disclosure design combined with a hybrid latent class model (HLCM). Results show that consumers' WTP for sustainable packaging builds cumulatively as information progresses from an abstract environmental claim, to physical interaction to full market presentation, indicating that environmental and sensory information reinforce rather than compete with each other. Two consumer segments emerge, "Low-engagement bidders" and "Premium green adopters", differing in their adoption of the sustainable option and in how strongly information shifts their valuation.For the consumption objective, a cross-sectional online survey conducted independently in Spain, Turkey, and Morocco was analyzed using country-specific hybrid latent class models and pooled direct-effects regressions to test formally for cross-country differences in adherence to the Mediterranean diet (AMD). Two comparable segments, "High adherents" and "Moderate adherents", are identified in each country, with nutrition knowledge as the only determinant common to all three. The remaining psychosocial, household, and sociodemographic drivers differ meaningfully across countries, showing that sustainable dietary behavior cannot be explained by a single, universal set of factors.Overall, the three chapters show that sustainability is valued conditionally rather than uniformly along the food value chain. Its value depends on the scale of the benefit at stake, on how sustainability information is communicated, and on who the individual is. Combining these three perspectives within one coherent value-chain narrative provides conceptual, methodological and empirical insight into how demand-side responses to sustainability actually operate throughout the food system, with practical implications for producers, retailers, consumers, and policymakers designing more effective, better-targeted sustainability interventions.

Last update: 22/10/2026 06:45:15.