Abstract: Risk analysis techniques help to identify threats, problems and anomalies in processes and systems that may affect the health and safety of employees, the environment, equipment reliability and normative compliance. In this thesis, a stochastic risk analysis with Monte Carlo simulation applied to FMECA (Failure Modes, Effects and Criticality Analysis) was performed for a specific model of wind turbine located in the Alpha Ventus offshore wind Park. Monte Carlo simulations provide the decisionmaker with a range of possible outcomes and the probabilities of occurrences of these results, that are derived mainly from the uncertainty associated to the subjectivity of judgement from a group of experts. As a supplementary method to the stochastic FMECA, Risk Assessments Matrix (RAM) was also used for bivariate distributions of Severity and Occurrence of the identified failure modes. The results obtained were compared to the results that would be normally obtained with a conventional FMECA and RAM and will serve in future steps to develop Risk-Based Inspection plans for this model of wind turbine in this offshore wind park.

Abstract: The author sets out a strategy for the sustainable development of a geothermal resource park within the geo-thermoelectric field Domo San Pedro (DSP), Nayarit, Mexico. The DSP project is owned by Grupo Dragón (GD), which it is the first private geothermal project within the country. The resource park is a pioneer initiative for sustainable large-scale geothermal direct-use development in the country. Currently, initiatives toward the development of geothermal direct-use projects, using the excess heat of the electricity generation, are being taken at the location. A fish farming project and a greenhouse are being developed under the Social Responsibility area, and a fruit drying pilot project is taking place in collaboration with the Engineering Institute of the National Autonomous University of Mexico (UNAM). A coordinated long-term value proposal is recommended to achieve resource allocation efficiency and stakeholder engagement through the process. The DSP RP strategy looks for a holistic sustainable, efficient and inclusive way of using the geothermal resource, implicating directuse development, socioeconomic benefits for the local community of San Pedro Lagunillas and an environmental industrial solution for all the parties involved. A symbiotic relationship among parties is proposed, fostering the sustainable development of geothermal low enthalpy projects in Mexico. The author presents a background analysis that will define the DSP Resource Park proposal, as well as a development strategy that will ensure the long-term success of the park. Resumen: La autora plantea una estrategia para el desarrollo sostenible de un parque de recursos geotérmicos dentro del campo geotermoeléctrico Domo San Pedro (DSP), Nayarit, México. El proyecto DSP es propiedad del Grupo Dragón (GD), siendo el primer desarrollo geotérmico privado del país. El parque de recursos es una iniciativa pionera para el desarrollo sostenible de los recursos geotérmicos de uso directo en México. Se han venido desarrollando iniciativas para el desarrollo de proyectos de uso directo geotérmico asociados a la planta, aprovechando el exceso de calor de la generación eléctrica. El departamento de Responsabilidad Social desarrolla un proyecto de piscicultura y un invernadero, así como un proyecto piloto de secado de frutas en colaboración con el Instituto de Ingeniería de la Universidad Nacional Autónoma de México (UNAM). La tesis plantea una propuesta de valor coordinada a largo plazo para lograr la eficiencia en la asignación de recursos y la participación de las partes interesadas a lo largo del proceso de establecimiento del parque de recursos. La estrategia busca una forma holística sostenible, eficiente e inclusiva de utilizar el recurso geotérmico, implicando el desarrollo de usos directos, beneficios socioeconómicos para la comunidad local de San Pedro Lagunillas y una solución ambiental industrial para todas las partes involucradas. Se propone una relación simbiótica entre las partes, fomentando el desarrollo sustentable de proyectos geotérmicos de baja entalpía en México. Así, la autora presenta un análisis de antecedentes que definirá la propuesta del parque de recursos, así como una estrategia de desarrollo que asegurará el éxito a largo plazo de la iniciativa.

Abstract: This dissertation explores how three applications of AI, namely classifiers, natural language processing and generative AI, have contributed and might contribute to the realisation of Richard Susskind’s predictions for the future of legal practice, as set in his 2023 book ‘Tomorrow’s Lawyers’. Besides reviewing Susskind’s predictions, it proposes a model to describe how lawyers work and uses it to identify existing uses of technology in the legal field. Also, it provides a general and non-technical approach to AI and its history and main features. With this context, it delves into the selected AI applications and explores how they work, their early legal applications, and their challenges. The research’s conclusion highlights the selected AI applications as powerful enablers for Susskind’s predictions. Moreover, how generative AI has the potential to disrupt the market of legal services and outperform lawyers in multiple tasks. A key insight refers to the latent market of underserved individuals, communities, and small and medium enterprises and how it offers an unbeatable opportunity for online-embedded generative AI-based legal services, which would improve access to legal services. Finally, this dissertation suggests an unavoidable shift in the general comprehension of the abbreviations ‘LL.M’ and ‘LLM’ within the legal field.

Abstract: With growing traffic demands and structural degradation accelerated by climate change, there is a critical need for continued advancement of concrete repair and strengthening technologies to enable extended bridge service life. Specifically, transitioning to cost-effective reinforced concrete (RC) retrofit strategies that further enhance durability under service loading conditions and minimize damage development under extreme hazard conditions, which are more probable to occur in longer-lasting concrete structures, are key elements of next-generation concrete bridges. This thesis explores the use of iron-based shape memory alloys (Fe-SMAs) as an active shear retrofit strategy for RC components. SMAs are a class of smart materials with the unique property of the shape memory effect, allowing them to fully recover plastic deformations when subsequently heated. By installing prestrained Fe-SMA strips and activating the shape memory effect, an active pressure can be introduced to help close cracks and apply a confining stress in the concrete. The primary objective is to evaluate the performance and practicality of Fe-SMA as an active shear strengthening technique in comparison to traditional methods such as external steel reinforcement and fibre-reinforced polymer (FRP) composites. The experimental phase of this study involved conducting push-off tests on RC specimens retrofitted with pre-deformed Fe-SMA strips. The goal was to assess the efficiency of active shear retrofitting. Additionally, finite element analysis (FEA) simulations were employed to model Fe SMA-retrofitted RC structures and investigate their behaviour under shear loading conditions. Key findings indicate that Fe-SMA retrofits, through transverse prestressing as part of the active retrofit strategy, contributed to notable improvements in the stiffness and strength of damaged RC components. Similar to passive retrofit methods, increased shear capacity was observed with higher levels of transverse reinforcement. Notably, combinations of substantial shear reinforcement ratios and elevated transverse prestressing provided the most significant gains in shear strength. Furthermore, the addition of prestressed Fe-SMA retrofits was found to effectively reduce shear crack widths and mitigate the progression of subsequent shear crack width growth. This study not only demonstrates the potential of Fe-SMA as a promising solution for active shear strengthening but also contributes to the development of future field-scale tests. The presence of Fe-SMA in damaged structures offers the prospect of multiple improvements, marking a significant advancement in the field of shear retrofitting.

Resumen: El estudio resalta la oportunidad para fortalecer la biodiversidad, especialmente a través de la enseñanza de las ciencias ambientales y biociencias con un impacto esperado en el aumento en los niveles de alfabetización científica y su relación bidireccional con la educación y la cultura ambiental en la sociedad colombiana, con foco de estudio e impacto en la población estudiantil de ciencias naturales, biología y educación ambiental en los tres niveles educativos, así como la comunidad académica de las áreas de bioeconomía, ecosistemas naturales y territorios sostenibles. Se realizó una revisión y selección de la literatura científica bajo criterios de inclusión y exclusión como el año de publicación, los idiomas, los países de publicación y/o ejecución del estudio y las bases de datos utilizadas. Así, se tuvieron en cuenta autores que publicaran trabajos de investigación cualitativa y cuantitativa tanto aplicada como no aplicada tales como Boff, Caamaño, Calixto Flores, García & Rodríguez, González González y López & Sánchez. Se encontraron numerosas tendencias, tensiones y limitaciones en la integración de los conceptos de educación ambiental, biodiversidad y alfabetización científica, que se presentan como desafíos en el qué, el para qué y la estructura de la educación ambiental; las definiciones y la enseñanza en la alfabetización científica y la complejidad y relevancia en la comprensión de la biodiversidad. Se hallaron herramientas y conceptualizaciones con un impacto positivo para la conservación de los ecosistemas naturales estratégicos del país como la transversalización de la educación ambiental en el currículo; y el uso sostenible de los recursos por parte de las comunidades y la resignificación y apropiación del ambiente dentro de la cultura de las regiones como las necesidades ambientales contextuales de las poblaciones alfabetizadas. Se resalta el abordaje amplio y complejo de la biodiversidad dentro de un planteamiento nuevo hacia una pedagogía ambiental.

Abstract: The Fourth Industrial Revolution has increased data exchange among organizations, creating security and privacy concerns. Federated Learning (FL) has emerged as a popular approach to address these challenges by enabling privacy-preserving machine learning (ML) on sensitive data. FL is an ML approach where models are trained locally on decentralized data, allowing organizations to collaborate and learn from each other’s data without sharing it, thereby ensuring data privacy and security. This thesis explores the application of FL for arrhythmia classification based on 12-lead ECGs while preserving medical records privacy. The performance of the FL models is evaluated under different degrees of heterogeneity in client labels, so-called Independent and Identically distributed or IID. The methodology involves a global server coordinating local modules and a four-step processing pipeline, where the local models’ weights are aggregated at the global server, and the resulting model is sent back to the clients (hospitals). The study achieves competitive classification performance for IID and non-IID data, along with a 28% reduction in training time compared to state-of-the-art methods. The findings have implications for privacy-preserving ML and early detection and diagnosis of cardiac diseases. Finally, the thesis demonstrates the FL’s effectiveness for accurate arrhythmia classification while preserving medical records’ privacy, making it a valuable tool in healthcare applications and contributing to advancing the Data Science field. Resumen: La Cuarta Revolución Industrial ha aumentado el intercambio de datos entre organizaciones, generando preocupaciones en cuanto a seguridad y privacidad. El Aprendizaje Federado (AF) ha surgido como un enfoque popular para abordar estos desafíos al permitir el aprendizaje automático (AA) preservando la privacidad en datos sensibles. El AF es un enfoque de AA en el que los modelos se entrenan localmente en datos descentralizados, lo que permite a las organizaciones colaborar y aprender de los datos de los demás sin compartirlos, garantizando así la privacidad y seguridad de los datos. Esta tesis explora la aplicación del AF para la clasificación de arritmias basada en electrocardiogramas (ECG) de 12 derivaciones, preservando al mismo tiempo la privacidad de los registros médicos. El rendimiento de los modelos de AF se evalúa bajo diferentes grados de heterogeneidad en las etiquetas de los clientes, conocido como Independiente e Idénticamente Distribuido o IID. La metodología implica un servidor global que coordina módulos locales y un proceso de cuatro pasos, donde los pesos de los modelos locales se agregan en el servidor global, y el modelo resultante se envía de vuelta a los clientes (hospitales). El estudio logra un rendimiento de clasificación competitivo para datos IID y no IID, junto con una reducción del 28% en el tiempo de entrenamiento en comparación con los métodos más avanzados. Los hallazgos tienen implicaciones para el AA preservando la privacidad y la detección temprana y diagnóstico de enfermedades cardíacas. Finalmente, la tesis demuestra la eficacia del AF para una clasificación precisa de arritmias preservando la privacidad de los registros médicos, convirtiéndolo en una herramienta valiosa en aplicaciones de atención médica y contribuyendo al avance del campo de la Ciencia de Datos.

Se considera la obra de teatro español que más ha llegado a los platós y a las tablas. Cuenta la historia de una pareja fugitiva en Andalucía.

Febrero 5 Deseo bajo los Olmos / Eugenio O'Nell -- Febrero 6 Arlequinada / Terence Rattigan -- Febrero 7 Arlequinada -- Febrero 8 Arlequinada -- Febrero 9 Arlequinada -- Febrero 10 Pluff, el fantasmita / Arlequinada / María Clara Machado -- Febrero 1Pluff, el fantasmita / Arsénico y encaje antiguo / Otto Kesselring

Returning to the original vocation of the philosophical task and to that defined by the phenomenology of Husserl, we propose to renew the approach towards the experience of falsehood starting from the Greco-Roman to restore a structure to thought and life based on the consideration of a series of «spiritual exercises» and Husserlian phenomenology to go « back to the 'things themselves». This philosophical-phenomenological project seeks to make us understand that in the face of falsehood, a rigorous thought, devoid of prejudices, can be envisaged by suggesting a tension towards a style of «philosophical life», which is consistent with our relationship to others and in our way of life, that is, from an ethical-phenomenological tension capable of placing the body as a psychoanalytic object par excellence according to Sartre’s assertion. Resumen: Retomando la idea de Husserl de ver la filosofía como una vocación de ver las cosas como ""ellas mismas son"", el trabajo de tesis se propone replantear el concepto de ""mentira"" y brindar un acercamiento filosófico a la experiencia de la mentira a partir de la época grecorromana y hasta nuestra época para ofrecer una estructura novedosa al planteamiento del tema y a la vivencia de la mentira basada en la consideración de la puesta en práctica de una serie de « ejercicios espirituales» y de fenomenología husserliana para volver « a las ""cosas mismas"", ofreciendo una alternativa de pensamiento dirigida hacia un estilo de «vida filosófica», que sea coherente con nuestra relación con los demás y con nuestro modo de vida, es decir, desde una tensión ético-fenomenológica capaz de situar al cuerpo como objeto psicoanalítico por excelencia según la afirmación de Sartre.

Abstract: Pharmaceutical Compounds (PCs), are synthetic substances intensively consumed by humans and continuously released into the urban wastewater. Often, the removal of PCs from wastewater is found to be insufficient, or even absent if population is not connected to the sewage system and wastewater is directly emitted into the environment. In the last decades, the PCs have aroused special attention because they have been found in surface water at concentrations that can cause negative effects on the aquatic biota and environment. Studies employing PCs monitoring and modelling techniques on surface waters have been mainly conducted in developed countries, while studies in developing countries are still scarce. Attempting to fill this gap, this work focused specifically in Colombia, where the occurrence of PCs has not been thoroughly studied and monitoring plans are barely done due to high costs. This work employed the Exposure to Pharmaceuticals in the Environment (ePiE), a spatially distributed model only implemented and used in Europe, to predict concentrations of PCs in rivers and lakes by considering their consumption, fate and transport. So, it aimed to create a new ePiE model network taking as a case of study the prediction of Naproxen and Paracetamol in the Upper Catchment of the Cauca River, Colombia, an area with a different socio-economic and climatic conditions with respect to the European continent. The methodological approach included: a) data collection on PCs’ consumption, physicochemical properties and removal in Wastewater Treatment Plants (WWTPs); b) consolidation of geodata of the study area such as hydrology, hydrography, urban agglomeration and WWTPs location; c) model building by using the open-source software environment R; d) analysis of the model sensitivity to input parameters; e) model validation. The results showed that all concentration predictions deviate within a range of only one order of magnitude from measurements which is a remarkable result when dealing with such high uncertainties. In terms of magnitude prediction, for the worst-case scenario, Naproxen and Paracetamol were found to exceed the environmental standards thresholds in 9% and 1% of the model river network nodes respectively. Overall, this research aimed to assess the applicability of the ePiE model in estimating the presence of PCs in a subregion of Colombia, demonstrating that it is feasible to expand ePiE to other areas and giving insights about the spatial distribution of pharmaceuticals substances so hotspots can be identified and several scenarios can be built in future works.