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Tesi en modalitat de compendi de publicacions, (English) Natural movements of Earth and human-based activities generate continuous ground displacements, affecting human life and the economy. In order to mitigate and reduce the impact of such displacements, accurate and frequent monitoring of their spatial and temporal characteristics is necessary. Remote sensing technology offers diverse temporal and spatial assets that are suitable for actively monitoring and measuring the intensity of ground displacements. This information can then be used to prevent risks, forecast occurrences, and manage the associated hazards. One active remote sensing system, known as synthetic aperture radar (SAR), enables accurate and anytime measurements of ground displacements using a technique called interferometric SAR (InSAR). InSAR allows for millimeter scale monitoring of deformations over small to wide areas affected by geohazards such as earthquakes, volcanic activities, landslides, and subsidence. With the increasing availability of large data, short revisits, and higher spatial resolution of SAR images, a massive amount of InSAR products is now provided. These products need to be properly analyzed and prepared for ground displacements monitoring to offer valuable information for geohazard risk management and monitoring. This PhD study is pursuing development of methodologies to model ground displacements behavior of InSAR time series (TS). Hence, an empirical analysis and modeling of displacements TS is performed to investigate various and dominant trends using robust statistical approaches. To achieve this, an automatic classification tool for InSAR TS is proposed to accurately classify seven types of displacement trends using step-wise statistical tests and conditions. The proposed workflow is applied and evaluated on simulated and real InSAR TS datasets from Barcelona and three landslide cases in Granada, Spain. Furthermore, the study explores the idea of modeling and classifying displacement TS through machine learning (ML, (Español) Los movimientos del terreno de origen natural y antrópico afectan la vida humana y la economía. Con el fin de mitigar y reducir el impacto de tales desplazamientos, es necesario realizar un monitoreo preciso y frecuente de sus características espaciales y temporales. La técnicas de teledetección ofrecen diversos recursos temporales y espaciales adecuados para monitorear activamente y medir la intensidad de los desplazamientos del suelo. Esta informaci ón puede ser utilizada para prevenir riesgos, pronosticar eventos y gestionar los peligros asociados. En particular, los sistemas de teledetecci ón acticva radar de apertura sintética (SAR), permiten mediciones precisas en cualquier momento de los desplazamientos del suelo utilizando una técnica llamada interferometría SAR (InSAR). La InSAR permite el monitoreo a escala milimétrica de deformaciones en áreas afectadas por peligros geol ógicos como terremotos, actividades volcánicas, deslizamientos de tierra y subsidencia. La creciente disponibilidad de grandes conjuntos de datos y una mayor resoluci ón espacial y temporal de las imágenes SAR, ha permitido la generación masiva de productos InSAR. Estos productos deben ser correctamente analizados y preparados para el monitoreo de desplazamientos del suelo con el fin de ofrecer informaci ón valiosa para la gesti ón y el monitoreo de riesgos geol ógicos. Este estudio de doctorado persigue el desarrollo de metodologías para la explotaci ón de series temporales INSAR para mejorar el control de los desplazamientos del suelo en series temporales de InSAR. Con este fin se han realizado análisis empírico y estadístico de las series temporales con el fin de investigar las tendencias dominantes. Para lograr esto, se proponen herramientas de clasificaci ón automática para las series temporales de InSAR a fin de clasificar con precisi ón siete tipos de tendencias de desplazamiento utilizando pruebas y condiciones estadísticas escalonadas. El flujo de trabajo propuesto se a, Postprint (published version)

BACKGROUND: A SARS-CoV-2 controlled human infection model (CHIM) has been successfully established in seronegative individuals using a dose of 1×101 50% tissue culture infectious dose (TCID50) pre-alpha SARS-CoV-2 virus. Given the increasing prevalence of seropositivity to SARS-CoV-2, a CHIM that could be used for vaccine development will need to induce infection in those with pre-existing immunity. Our aim was to find a dose of pre-alpha SARS-CoV-2 virus that induced infection in previously infected individuals. METHODS: Healthy, UK volunteers aged 18-30 years, with proven (quantitative RT-PCR or lateral flow antigen test) previous SARS-CoV-2 infection (with or without vaccination) were inoculated intranasally in a stepwise dose escalation CHIM with either 1×101, 1×102, 1×10³, 1×104, or 1×105 TCID50 SARS-CoV-2/human/GBR/484861/2020, the same virus used in the seronegative CHIM. Post-inoculation, volunteers were quarantined in functionally negative pressure rooms (Oxford, UK) for 14 days and until 12-hourly combined oropharyngeal-nasal swabs were negative for viable virus by focus-forming assay. Outpatient follow-up continued for 12 months post-enrolment, with additional visits for those who developed community-acquired SARS-CoV-2 infection. The primary objective was to identify a safe, well tolerated dose that induced infection (defined as two consecutive SARS-CoV-2 positive PCRs starting 24 h after inoculation) in 50% of seropositive volunteers. This study is registered with ClinicalTrials.gov (NCT04864548); enrolment and follow-up to 12 months post-enrolment are complete. FINDINGS: Recruitment commenced on May 6, 2021, with the last volunteer enrolled into the dose escalation cohort on Nov 24, 2022. 36 volunteers were enrolled, with four to eight volunteers inoculated in each dosing group from 1×101 to 1×105 TCID50 SARS-CoV-2. All volunteers have completed quarantine, with follow-up to 12 months complete. Despite dose escalation to 1×105 TCID50, we were unable to

The barren plateau phenomenon, characterized by loss gradients that vanish exponentially with system size, poses a challenge to scaling variational quantum algorithms. Here we explore the potential of warm starts, whereby one initializes closer to a solution in the hope of enjoying larger loss variances. Focusing on an iterative variational method for learning shorter-depth circuits for quantum real and imaginary time evolution we conduct a case study to elucidate the potential and limitations of warm starts. We start by proving that the iterative variational algorithm will exhibit substantial (at worst vanishing polynomially in system size) gradients in a small region around the initializations at each time-step. Convexity guarantees for these regions are then established, suggesting trainability for polynomial size time-steps. However, our study highlights scenarios where a good minimum shifts outside the region with trainability guarantees. Our analysis leaves open the question whether such minima jumps necessitate optimization across barren plateau landscapes or whether there exist gradient flows, i.e., fertile valleys away from the plateau with substantial gradients, that allow for training., Comment: 9 + 26 pages, 5 + 2 figures

(English) The availability of RADAR images for monitoring Earth’s surface and its changes over time prompt the development of several interferometric SAR (InSAR) algorithms and methodologies. The Multi-temporal InSAR (MT-InSAR) techniques- being the one, generally, aim at extracting the temporal evolution of displacements of targets with coherent scattering behavior from a stack of SAR acquisitions that are taken from the same area and processed relative to temporal and spatial references. On the other hand, due to the complex nature of SAR measurements, addressing all possible model parameters in a comprehensive manner is still challenging and is a source of error and misinterpretation. As a well-established spaceborne geodetic technique with various applications, the accuracy of MT-InSAR products does matter, and hence potential error sources must be mitigated. The accuracy of displacement time-series (TS) estimates, in many cases, is governed by the quality and number of exploited images, associated interferograms, and selected measurement points. Such data and derived results are heavily influenced by factors, including environmental conditions at times of measurement acquisition and the processing algorithms used. Incomplete representations of factors in modeling MT-InSAR measurements may, as well, lead to omission errors and hence an incorrect estimation and interpretation of the displacement TS. Any uncertainty in the accuracy of the MT-InSAR products compromises their reliability in sensitive applications. In line with this, tools to detect, identify, and classify interferometric measurements that are particularly impacted by phase unwrapping (PhU) errors had been developed with the aim to identify and mitigate potential errors in the data— ultimately improving the quality and valuing the worth of MT-InSAR measurements. These tools make use of estimated phase residuals derived from a redundant network of unwrapped interferograms to formulate threshold-based, (Español) La creciente disponibilidad de imágenes RADAR para monitorear la superficie de la Tierra y sus cambios a lo largo del tiempo ha impulsado el desarrollo de varios algoritmos y metodologías de SAR (Synthetic Aperture Radar) interferométrico (InSAR). Las conocidas técnicas Multi-temporales InSAR (MT-InSAR) tienen como objetivo extraer la evolución temporal de los desplazamientos de puntos medidos. Esto se realiza sobre blancos con un comportamiento coherente en una pila de adquisiciones SAR sobre la misma área. Debido a la naturaleza compleja de las mediciones SAR, abordar todos los parámetros posibles del modelo de manera integral sigue siendo un desafío y es una fuente de error. Como una técnica geodésica bien establecida, la precisión de los productos MT-InSAR es importante y, por lo tanto, se deben mitigar las posibles fuentes de error. Cualquier incertidumbre en la precisión de los productos MT-InSAR compromete su fiabilidad en aplicaciones sensibles. En línea con esto, en esta tesis se han desarrollado herramientas para detectar, identificar y clasificar las mediciones interferométricas que se ven particularmente afectadas por los errores de desarrollo de fase, con el objetivo de identificar y mitigar posibles errores en los datos, mejorando entonces la calidad de las mediciones de MT-InSAR. Estas herramientas utilizan residuos de fase estimados derivados de una red redundante de interferogramas enrollados para formular índices de calidad basados en umbrales como indicadores de la fiabilidad de los puntos de medición, de las imágenes en una serie temporal, en imágenes y en interferogramas. Las técnicas se aplicaron píxel por píxel en el espacio y en el tiempo para asociar índices de fiabilidad que resaltaran la influencia de los errores de desarrollado de fase globales y locales. Los experimentos corroboran la estrategia propuesta. Otro aspecto importante de la tesis es el uso de la herramienta de índice de fiabilidad como técnica de selección de píxele, Postprint (published version)

El aprendizaje automático, o machine learning, ha entrado con fuerza en el ámbito de la investigación, transformando la forma en que los científicos y académicos abordan sus problemas. Se puede considerar como un nuevo pilar en el proceso de la investigación científica, complementando las metodologías tradicionales. En los últimos años, el machine learning ha demostrado ser una herramienta esencial en el análisis de grandes conjuntos de datos, descubrir patrones y extraer conocimientos valiosos que de otra forma se nos escaparían. Además, la programación permite, cada vez más, automatizar procesos que de otra forma serían áreas tediosas y repetitivas, liberando así tiempo para realizar otras actividades de investigación. Las imágenes satelitales y los datos del Sistema de Identificación Automática (AIS, siglas en inglés) de las embarcaciones son dos fuentes valiosas de información en el ámbito de la navegación. Las imágenes radar ofrecen una visión de la ubicación y el movimiento de las embarcaciones independientemente de las condiciones climáticas o de iluminación. Por otro lado, los datos AIS son un sistema de seguimiento cada vez implantado en más buques y navíos de recreo que transmiten datos clave, como la identificación, rumbo, velocidad y medidas de la embarcación, enviados regularmente y recibidos por barcos y puertos mundialmente. Dentro de este marco se busca realizar un trabajo de investigación centrado en estas tecnologías. Dicho trabajo tiene dos partes diferenciadas. En la primera parte creamos un código que cruce datos AIS con las imágenes satelitales cerciorándonos de que realmente las embarcaciones aparezcan y se vean y la creación de etiquetas mediante buffers para alimentar posteriormente al modelo de predicción basado en un aprendizaje supervisado. Todo el proceso de obtener los datos, procesarlos con distintos filtros, crear los buffers y la comprobación de que las etiquetas están realmente bien, ya que si se alimenta erróneamente el algoritmo n

Els nematodes (del grec Nematoda) són un dels grans fils taxonòmics en el que es classifiquen els invertebrats dins del regne animal. Els nematodes fitoparàsits generen grans pèrdues econòmiques als cultius. Concretament parlant, el gènere Meloidogyne, nematode formador d’agalles (RKN), és el que genera una amenaça més gran arreu del món. A dia d’avui hi ha la necessitat de desenvolupar i executar noves solucions, és a dir mètodes de control, que siguin efectius i a la vegada tinguin un menor impacte pel medi ambient i, sobretot, pel benestar humà i animal. Aquests darrers anys s’han estudiat diferents estratègies per tal de controlar-los. Aquest projecte és presenta amb l’objectiu caracteritzar la capacitat de diferents agents inductors de resistència a Meloidogyne spp. en cultius d'hortalisses de solanàcies, cucurbitàcies, compostàcies, quenopodiàcies i lleguminoses. Els inductors de resistència que es van fer servir en aquest experiment foren el PRIMTAL WF i el VACCIPLANT MAX. EL PRIMTAL és un activador immunològic, què es basa en el ‘priming’ per augmentar la capacitat de resposta del seu sistema immunològic; i el VACCIPLANT és un fungicida biològic d’origen natural que actua com a inductor i activador de mecanismes d’autodefensa de la planta. L’experiment es realitzà en condicions controlades en cambres de creixement, en les quals es cultivaren germoplasmes de tomàquet susceptible, tomàquet resistent, pebrot, albergínia, cogombre, carbassó, síndria, enciam, bledes i fesols. L’espècie de nematode feta servir en els experiments mencionats va ser M. incognita procedent de l’agròpolis de la col·lecció de l’UPC. Cada germoplasma tenia 45 repeticions, 15 del grup control (sense tractament), 15 amb tractament PRIMTAL i 15 amb tractament VACCIPLANT. Es va fer el recompte d’ous per planta per calcular el nivell d’infecció, el nombre d’ous per massa d’ou per tal de calcular la fertilitat femenina i, per últim, el nombre d’ous per planta per tal d’estimar el nivell de res, Nematodes (from the Greek Nematoda) are one of the major taxonomic phyla in which invertebrates are classified within the animal kingdom. Phytoparasitic nematodes cause great economic losses to crops. Specifically speaking, the genus Meloidogyne, gall-forming nematode (RKN), is the one that generates the greatest threat around the world. Today there is a need to develop and implement new solutions, that is to say control methods, which are effective and at the same time have a lower impact on the environment and, above all, on human and animal welfare. In recent years, different strategies have been studied in order to control them. This project is presented with the aim of characterizing the ability of different agents to induce resistance to Meloidogyne spp. in vegetable crops of solanaceae, cucurbitaceae, compostaceae, chenopodiaceae and legumes. The resistance inducers used in this experiment were PRIMTAL WF and VACCIPLANT MAX. PRIMTAL is an immune activator, which is based on "priming" to increase the response capacity of your immune system; and VACCIPLANT is a biological fungicide of natural origin that acts as an inducer and activator of the plant's self-defense mechanisms. The experiment was carried out under controlled conditions in growth chambers, in which germplasm of susceptible tomato, resistant tomato, pepper, eggplant, cucumber, courgette, watermelon, lettuce, Swiss chard and beans were cultivated. The species of nematode used in the aforementioned experiments was M. incognita from the agropolis of the UPC collection. Each germplasm had 45 repetitions, 15 from the control group (no treatment), 15 with PRIMTAL treatment and 15 with VACCIPLANT treatment. Egg counts per plant were done to calculate infection level, number of eggs per egg mass to calculate female fertility, and finally number of eggs per plant to calculate 'estimate the level of resistance. The results showed that the nematode had difficulty infecting resistant chard, cucumber, bean, let, Els nematodes (del grec Nematoda) són un dels grans fils taxonòmics en el que es classifiquen els invertebrats dins del regne animal. Els nematodes fitoparàsits generen grans pèrdues econòmiques als cultius. Concretament parlant, el gènere Meloidogyne, nematode formador d’agalles (RKN), és el que genera una amenaça més gran arreu del món. A dia d’avui hi ha la necessitat de desenvolupar i executar noves solucions, és a dir mètodes de control, que siguin efectius i a la vegada tinguin un menor impacte pel medi ambient i, sobretot, pel benestar humà i animal. Aquests darrers anys s’han estudiat diferents estratègies per tal de controlar-los. Aquest projecte és presenta amb l’objectiu caracteritzar la capacitat de diferents agents inductors de resistència a Meloidogyne spp. en cultius d'hortalisses de solanàcies, cucurbitàcies, compostàcies, quenopodiàcies i lleguminoses. Els inductors de resistència que es van fer servir en aquest experiment foren el PRIMTAL WF i el VACCIPLANT MAX. EL PRIMTAL és un activador immunològic, què es basa en el ‘priming’ per augmentar la capacitat de resposta del seu sistema immunològic; i el VACCIPLANT és un fungicida biològic d’origen natural que actua com a inductor i activador de mecanismes d’autodefensa de la planta. L’experiment es realitzà en condicions controlades en cambres de creixement, en les quals es cultivaren germoplasmes de tomàquet susceptible, tomàquet resistent, pebrot, albergínia, cogombre, carbassó, síndria, enciam, bledes i fesols. L’espècie de nematode feta servir en els experiments mencionats va ser M. incognita procedent de l’agròpolis de la col·lecció de l’UPC. Cada germoplasma tenia 45 repeticions, 15 del grup control (sense tractament), 15 amb tractament PRIMTAL i 15 amb tractament VACCIPLANT. Es va fer el recompte d’ous per planta per calcular el nivell d’infecció, el nombre d’ous per massa d’ou per tal de calcular la fertilitat femenina i, per últim, el nombre d’ous per planta per tal d’estimar el nivell de res

Badlands are considered hotspots of sediment production, contributing to large fractions of the sediment budget of catchments and river basins. The erosion rates of these areas can exceed 100 t ha-1 y-1, leading to significant environmental and economic impacts. This research aims to assess badland susceptibility and the relevance of its governing factors at different spatial scales using the well-known machine learning approach random forest (RF). The Upper Llobregat River Basin (ULRB, approx. 500 km2) and Catalonia (approx. 32,000 km2) have been selected as study areas. Previous studies stated that the RF approach is successful at making predictions for the same area where it has been trained, but the results of testing it in a different area remains unexplored. This work aims to evaluate the feasibility of upscaling to the large region of Catalonia a RF model trained in the small ULRB area. Two badland datasets of both small and large regions and a total of eleven governing factors have been used to determine the areas susceptible to badlands. Models performance has been analyzed through three different evaluation metrics: overall accuracy, kappa coefficient and area under receiver operating characteristic curve (AUC). The outcomes of this work confirmed that RF is a powerful tool for badland susceptibility analysis, specially when predictions are made in the same scale and spatial context where the model has been trained. Upscaling a RF model defined in the ULRB to the large area of Catalonia has been possible, but improved results have been obtained when the training of the models has directly been performed in the large region. Our final RF modelling results have facilitated the development of a large scale (32,000 km2) Badland Susceptibility Map for the full extension of Catalonia with a predictive overall accuracy of 97%, which strongly emphasizes lithology and Normalized Difference Vegetation Index (NDVI) as the main conditioning factors of badland distribu, This publication is part of the grant EROSLOP I + D + i project funded by MCIN/AEI/10.13039/501100011033/and, as appropriate, by “ERDF A way of making Europe” by the “European Union” or by the “European Union NextGenerationEU/PRT”. We acknowledge the E-OBS dataset and the data providers in the ECA&D project (https://www.ecad.eu). Mariano Moreno-de-las-Heras is beneficiary of a Serra Hunter fellowship on Physical and Environmental Geography funded by the Generalitat de Catalunya (UB-LE-9055), and grant 2021SGR00859, awarded by the Agència de Gestió d'Ajuts Universitaris i de Recerca de la Generalitat de Catalunya (SGR2021-2024)., Peer Reviewed, Objectius de Desenvolupament Sostenible::15 - Vida d'Ecosistemes Terrestres, Postprint (author's final draft)

Es publiquen per ordre cronològic els regests de dos registres de l’Arxiu Diocesà de Girona que, entre altres documents, contenen moltes escriptures referents a la percepció, la gestió, la transmissió i la venda de l’impost del delme., The abstracts of two registers of the Diocesan Archives of Girona are published in chronological order, which, amongst other documents, contain many scriptures related to the perception, management, transmission and sales of the tithe tax.

There is no doubt that land cover and climate changes have consequences on landslide activity, but it is still an open issue to assess and quantify their impacts. Wanzhou County in southwest China was selected as the test area to study rainfall-induced shallow landslide susceptibility under the future changes of land use and land cover (LULC) and climate. We used a high-resolution meteorological precipitation dataset and frequency distribution model to analyse the present extreme and antecedent rainfall conditions related to landslide activity. The future climate change factors were obtained from a 4-member multi-model ensemble that was derived from statistically downscaled regional climate simulations. The future LULC maps were simulated by the land change modeller (LCM) integrated into IDRISI Selva software. A total of six scenarios were defined by considering the rainfall (antecedent conditions and extreme events) and LULC changes towards two time periods (mid and late XXI century). A physically-based model was used to assess landslide susceptibility under these different scenarios. The results showed that the magnitude of both antecedent effective recharge and event rainfall in the region will evidently increase in the future. Under the scenario with a return period of 100 years, the antecedent rainfall in summer will increase by up to 63% whereas the event rainfall will increase by up to 54% for the late 21st century. The most considerable changes of LULC will be the increase of forest cover and the decrease of farming land. The magnitude of this change can reach + 22.1% (forest) and –9.2% (farmland) from 2010 until 2100, respectively. We found that the negative impact of climate change on landslide susceptibility is greater than the stabilizing effect of LULC change, leading to an over decrease in stability over the study area. This is one of the first studies across Asia to assess and quantify changes of regional landslide susceptibility under scenarios drive, This study was funded by the National Natural Science Foundation of China (Grant No. 41972297), Talents in Hebei Provincial Education Office (Grant No. SLRC2019027), Natural Science Foundation of Hebei Province (Grant No. D2022202005), and the Spanish national project EROSLOP (Grant No. PID2019-104266RB-I00/AEI/10.13039/501100011033). Zizheng Guo acknowledges the financial support of China Scholarship Council for his research at UPC BarcelonaTECH. Joaquin Ferrer acknowledges support from the Erasmus + Joint Master Degree Programme Flood Risk Management funded by the European Commission and run by IHE Delft, No. 2018-1514. The China Meteorological Forcing Dataset is provided by the National Tibetan Plateau Data Center (https://data.tpdc.ac.cn/)., Peer Reviewed, Postprint (published version)

En aquest treball, després d'identificar la necessitat no coberta d'una embarcació de petites dimensions amb la qual fer treballs batimètrics, es proposa, dissenya, construeix i assaja una embarcació autònoma per a treballs batimètrics. S'entrarà detalladament tant en el disseny d'aquest tipus d'embarcació i els seus components, com en els mètodes constructius que s'ha hagut de desenvolupar per a poder realitzar aquest projecte. Durant la fase de disseny, una profunda anàlisi de les prestacions de cada part de l'embarcació és realitzat, incloent-hi anàlisi amb elements finits, CFD i mètodes experimentals. Finalment, dos plans batimètrics són realitzats en entorns reals per a demostrar les capacitats de topografia hidrològica de l'embarcació prototip i mitjançant les dades de navegació recollits durant l'execució de les batimetries es caracteritzarà l'abast i rendiment d'aquesta., En este trabajo, tras identificar la necesidad no cubierta de una embarcación de pequeñas dimensiones con la que realizar trabajos batimétricos, se propone, diseña, construye y ensaya una embarcación autónoma para trabajos batimétricos. Se entrará en detalle tanto en el diseño de este tipo de embarcación y sus componentes, como en los métodos constructivos que se ha tenido que desarrollar para poder realizar este proyecto. Durante la fase de diseño, un profundo análisis de las prestaciones de cada parte de la embarcación es realizado, incluyendo análisis con elementos finitos, CFD y métodos experimentales. Finalmente, dos planos batimétricos son realizados en entornos reales para demostrar las capacidades de topografía hidrológica de la embarcación prototipo y mediante los datos de navegación recogidos durante la ejecución de las batimetrías se caracterizará el alcance y rendimiento de la misma., In this work, after identifying the unmet need for a small vessel with which to do bathymetric work, an autonomous bathymetric work vessel is proposed, designed, built and tested. Therefore, it will go into detail both in the design of this type of vessel and its components, as well as in the constructive methods that had to be developed to be able to carry out this project. During the design phase, a deep analysis of the performance of each part of the vessel is carried out, including analysis with finite elements, CFD and experimental methods. Finally, two bathymetric plans are made in real environments to demonstrate the hydrological topography capabilities of the prototype vessel and through the navigation data collected during the execution of the bathymetry, the range and performance of this will be characterized.