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Cada año, crece la preocupación sobre el cambio climático y sus posibles repercusiones en nuestro planeta. Por esta razón, es fundamental realizar un análisis de la situación energética actual para comprender el comportamiento de los países y la sociedad que nos rodea. En este Trabajo de Fin de Grado, examinaremos la correlación existente entre el consumo de energía y el nivel de desarrollo de los países europeos analizando datos a nivel mundial para adquirir una mayor perspectiva, con el objetivo de proyectar posiblestendencias y estar preparados para las necesidades futuras, avanzando siempre hacia las ODS 3 (salud y bienestar) y ODS 7 (energía accesible y no contaminante). En este estudio se analizarán también las características de los países atendiendo a su nivel de desarrollo y se analizarán aspectos existentes en nuestra sociedad como las desigualdades, la contaminación o la brecha de género., Every year, there is a growing concern about climate change and its potential impact on our planet. For this reason, it is essential to analyse the current energy situation in order to understand the behaviour of the countries and society around us. In this Final Degree Project, we will examine the correlation between energy consumption and the level of development of European countries by analysing global data to gain a better perspective, with the aim of projecting possible trends and being prepared for future needs, always moving towards SDG 3 (health and well-being) and SDG 7 (accessible and clean energy). This study will also analyse the characteristics of the countries according to their level of development and will analyse existing aspects in our society such as inequalities, pollution or the gender gap., Departamento de Ingeniería de Sistemas y Automática, Grado en Ingeniería en Organización Industrial

The use of energy has played a fundamental role in social development since the beginning of humanity, allowing the expansion of its ecological niche through the utilization of the physical potentials available in nature. However, today we face the great challenge of reducing greenhouse gas emissions to mitigate climate change, which is the result of the massive use of fossil resources. The threat is enormous, the extinction of life forms and entire ecosystems that allow human beings to perpetuate themselves as a species. To mitigate this situation and sustain the level of social complexity achieved as civilization, part of the academia advocates for transitioning towards 100% renewable energy systems. Photovoltaic solar energy, onshore and offshore wind energy, and hydropower are the most mature technologies that support these decarbonization projections while electrifying the economy. However, an emerging discussion puts certain limits on their practicability and effectiveness through modelling and analysis. The stochastic and intermittent nature of renewable generation fluctuates the power output according to the instant resource available in the parks, variations that dramatically affect the traditional stability of the fossil-based electrical system. Despite the existence of numerous options to manage these fluctuations, there is not a single energy planning approach to meet the objectives. To assist in policy selection, Integrated Assessment Models (IAMs) have traditionally focused on global representations, resulting in limited temporal resolution that does not yield satisfactory results for energy analysis. These leave the responsibility to more technical models, specifically focused on renewable variability management. This problem is the main motivation of this doctoral thesis. Increasing the temporal resolution in IAMs has required experimentation in the fields of statistics, mathematics, and systems dynamics, among others. The results obtained in this th, El uso de la energía ha jugado un papel fundamental en el desarrollo social desde los comienzos de la humanidad, permitiendo la expansión de su nicho ecológico a través del aprovechamiento de los potenciales físicos disponibles en la naturaleza. Sin embargo, hoy día nos enfrentamos al gran reto de reducir las emisiones que generan efecto invernadero para paliar el cambio climático, fruto del uso masivo de recursos fósiles. La amenaza es enorme, la extinción de formas de vida y ecosistemas enteros que permiten al ser humano perpetuarse como especie. Para paliar esta situación y sostener el grado de complejidad social que hemos alcanzado como civilización, parte de la academia aboga por transitar hacia sistemas energéticos 100% renovables. Energía solar fotovoltaica, eólica terrestre y marina, y energía hidráulica son las tecnologías más maduras que sostienen la electrificación de la economía en estas proyecciones de descarbonización. Sin embargo, una discusión emergente pone ciertos límites a su practicidad y eficacia a través del modelado y análisis. Las estocásticas, intermitentes, generaciones renovables fluctuan la potencia de generación de acuerdo al recurso instantáneo presente en los parques, variaciones que afectan dramáticamente a la estabilidad tradicional del sistema eléctrico fosilista. A pesar de la existencia de numerosas opciones para gestionar estas fluctuaciones energéticas, no existe una única planificación energética para cumplir con los objetivos. Para ayudar en la selección de políticas, los modelos de evaluación integrada (IAM, por sus siglas en inglés de integrated assessment model) se han centrado tradicionalmente en representaciones globales, derivando en una escasa resolución temporal que no permite obtener resultados satisfactorios para el análisis energético, dejando a modelos más técnicos, específicos en la cuestión de la gestión de la variabilidad. Este problema es el principal motivo de esta tesis doctoral. Aumentar la resolución tempor, Escuela de Doctorado, Doctorado en Ingeniería Industrial

Objectives: More than two years into the COVID-19 pandemic, SARS-CoV-2 still remains a global public health problem. Successive waves of infection have produced new SARS-CoV-2 variants with new mutations for which the impact on COVID-19 severity and patient survival is uncertain. Methods: A total of 764 SARS-CoV-2 genomes, sequenced from COVID-19 patients, hospitalized from 19th February 2020 to 30 April 2021, along with their clinical data, were used for survival analysis. Results: A significant association of B.1.1.7, the alpha lineage, with patient mortality (log hazard ratio (LHR) = 0.51, C.I. = [0.14,0.88]) was found upon adjustment by all the covariates known to affect COVID-19 prognosis. Moreover, survival analysis of mutations in the SARS-CoV-2 genome revealed 27 of them were significantly associated with higher mortality of patients. Most of these mutations were located in the genes coding for the S, ORF8, and N proteins. Conclusions: This study illustrates how a combination of genomic and clinical data can provide solid evidence for the impact of viral lineage on patient survival.

Energy is a key input for achieving economic growth and satisfying human needs. The increase of economic activity has triggered a rise in energy demand with a large share of fossil fuels in the mix. The use of these finite resources has caused environmental impacts that could compromise earths’ future if current trends persist regarding energy use. Thus, modelling future trajectories of socioeconomic and energy systems considering biophysical limits is a key point for assessing the sustainability of these possible futures. This doctoral thesis depicts the complete development of an integrated assessment model (IAM) energy-economy-environment for Ecuador that will assess energy scenarios up to 2050, with a previous modelling of national oil extraction considering geological constraints. The model named EEDEC (Ecuador Energy Development under Energy Constraints) has been developed using a System Dynamics approach and includes new features compared to previous IAMs for Ecuador. Some of the features included are representation of biophysical limits regarding energy availability at national and global scale, and its feedback on the economy and energy use, and modelling oil export policies. Potentiality of the model was shown through simulation of BAU scenarios. If current trends persist, energy shortages might occur causing a potential recession by 2040. Despite its condition of oil producing country, Ecuador maintains a high dependency on fuel imports. Which makes the country vulnerable to changes in global supply of liquid fuels. EEDEC model also assessed energy transition scenarios for Ecuador. This analysis shows that maximum efforts are necessary for a profound change of the country’s energy system. Policies for massive deployment of renewables for electricity generation, energy efficiency improvement, reduction of oil exports, and introduction of new technologies are necessary for all economic sectors, especially transportation. Even if scenarios depict, La energía constituye un insumo fundamental para el crecimiento económico y la satisfacción de necesidades de la humanidad. Con el incesante incremento de la actividad económica, ha aumentado a su vez el consumo de energía, el cual actualmente tiene un predominio de fuentes fósiles finitas. Esto ha provocado impactos en el ambiente que pueden comprometer el futuro del planeta si no se modifican las tendencias actuales de comportamiento de sistemas socioeconómico y energético. En este sentido el modelado de las trayectorias futuras de estos sistemas considerando los límites biofísicos y sus relaciones es clave para evaluar la sostenibilidad de estos posibles caminos. La presente tesis doctoral describe el desarrollo completo de un modelo de evaluación integrada (IAM por sus siglas en inglés) energía-economía-ambiente para Ecuador, a través del cual se analizan escenarios energéticos a 2050, comenzando con el modelado de la extracción de petróleo nacional con base en restricciones geológicas. El modelo llamado EEDEC (Ecuador Energy Development under Energy Constraints) ha sido desarrollado utilizando la Dinámica de Sistemas e incorpora nuevas características respecto a los IAM desarrollados previamente. Algunas de ellas son: la representación de límites biofísicos de disponibilidad de energía tanto a nivel nacional como mundial y su retroalimentación en la economía nacional y el consumo de energía, y el modelado de políticas de exportación de petróleo. Las potencialidades del modelo han sido desarrolladas a través de la simulación de escenarios tendenciales que, de mantener su continuidad, generarían un déficit de oferta de energía, el cual impediría un crecimiento económico sostenido y una posible recesión. A pesar de ser un país productor de petróleo, Ecuador mantiene una alta dependencia de importación de derivados lo que lo hace vulnerable ante cambios en la oferta mundial de estos energéticos. El modelo EEDEC también ha permitido estudiar escenarios hacia la tran, Escuela de Doctorado, Doctorado en Ingeniería Industrial

Background and Objective: Prostate cancer is one of the most prevalent forms of cancer in men worldwide. Traditional screening strategies such as serum PSA levels, which are not necessarily cancer-specific, or digital rectal exams, which are often inconclusive, are still the screening methods used for the disease. Some studies have focused on identifying biomarkers of the disease but none have been reported for diagnosis in routine clinical practice and few studies have provided tools to assist the pathologist in the decision-making process when analyzing prostate tissue. Therefore, a classifier is proposed to predict the occurrence of PCa that provides physicians with accurate predictions and understandable explanations. Methods: A selection of 47 genes was made based on differential expression between PCa and normal tissue, GO gene ontology as well as the literature to be used as input predictors for different machine learning methods based on eXplainable Artificial Intelligence. These methods were trained using different class-balancing strategies to build accurate classifiers using gene expression data from 550 samples from ’The Cancer Genome Atlas’. Our model was validated in four external cohorts with different ancestries, totaling 463 samples. In addition, a set of SHapley Additive exPlanations was provided to help clinicians understand the underlying reasons for each decision. Results: An in-depth analysis showed that the Random Forest algorithm combined with majority class downsampling was the best performing approach with robust statistical significance. Our method achieved an average sensitivity and specificity of 0.90 and 0.8 with an AUC of 0.84 across all databases. The relevance of DLX1, MYL9 and FGFR genes for PCa screening was demonstrated in addition to the important role of novel genes such as CAV2 and MYLK. Conclusions: This model has shown good performance in 4 independent external cohorts of different ancestries and the explanations provided are