Quorum sensing governs bacterial communication, playing a crucial role in regulating population behaviour. We propose a mathematical model that uncovers chaotic dynamics within quorum sensing networks, highlighting challenges to predictability. The model explores interactions between autoinducers and two bacterial subtypes, revealing oscillatory dynamics in both a constant autoinducer sub-model and the full three-component model. In the latter case, we find that the complicated dynamics can be explained by the presence of homoclinic Shilnikov bifurcations. We employed a combination of normal form analysis and numerical continuation methods to analyse the system.

La caracterización detallada de los procesos de combustión es clave para optimizar la eficiencia energética y comprender la formación de emisiones; en llamas de difusión de etileno, la radiación constituye el mecanismo de transferencia de calor predominante y, al mismo tiempo, aporta información diagnóstica sobre la estructura térmica y química del sistema, no obstante, su diagnóstico cuantitativo es desafiante, ya que la señal medida combina la emisión continua del hollín con la emisión espectral selectiva de gases como CO₂ y H₂O; para abordar este problema, este trabajo propone una metodología de diagnóstico óptico que integra termografía infrarroja multiespectral con un modelo de transferencia radiativa hacia adelante (forward) de alta resolución, validada mediante el estudio de dos escenarios opuestos: la llama Yale 32, de baja carga de hollín, y la llama Yale 80, de alta carga de hollín; en particular, se incorpora explícitamente la autoabsorción mediante un enfoque línea por línea (LBL), lo que permite representar la atenuación de la radiación emitida en el núcleo al propagarse a través del propio medio antes de alcanzar el sensor, entregando una base radiativa físicamente consistente para el proceso de inversión; finalmente, la intensidad resultante se combina con las funciones de respuesta de los filtros de la cámara para generar imágenes sintéticas, permitiendo una validación directa frente a los datos experimentales; los resultados demuestran que las imágenes sintéticas reproducen la morfología observada y la ubicación de los máximos de intensidad en todos los canales, el análisis de sensibilidad espectral confirma que la temperatura (T) es el factor más influyente sobre la radiancia en todas las bandas, mientras que la contribución del hollín (fv) incrementa su dominancia hacia longitudes de onda más largas y bajo condiciones de alta carga de hollín; un hallazgo clave es la capacidad de distinguir la firma espectral de las especies mediante la selectividad de los filtros: se observó predominio de CO₂ en los canales 1 y 2, lo que favorece su detección y monitoreo en esos rangos, en contraste, el H₂O se manifiesta de forma más tenue a lo largo del espectro, con mayor incidencia en los filtros 1 y 4, mientras que el hollín domina en los filtros 3 y 4; a partir de estas observaciones, se concluye que la metodología logra un desacoplamiento efectivo de señales radiativas y que la integración entre el modelado físico y las características específicas de la cámara habilita un diagnóstico térmico y químico robusto, en particular, se obtiene consistencia entre los campos simulados de T, CO₂, H₂O y fv, identificando a T como el principal determinante de la radiancia y al hollín como un contribuyente de importancia creciente a longitudes de onda más largas y para altas concentraciones; asimismo, la diferenciación del rol de cada canal sustenta una estrategia de inversión escalonada: primero estimar los parámetros dominantes (T y fv) y posteriormente refinar las fracciones molares gaseosas; si bien el estudio reconoce limitaciones asociadas a las constantes ópticas, los resultados posicionan esta metodología como una herramienta viable para diagnósticos en escenarios industriales complejos y, como proyección, se propone abordar el problema inverso para desagregar cuantitativamente la contribución de las especies directamente desde la imagen experimental y extender este marco metodológico a sistemas de combustión de mayor complejidad.

El presente trabajo propone un enfoque basado en el método PBDW (Parameterized- Background Data Weak), utilizando elementos finitos, para reconstruir el campo de desplazamiento en el dominio completo a partir de datos parciales obtenidos mediante elastografía utilizando resonancia magnética. El método combina información de una base de simulaciones numéricas de tejido cerebral poro-elástico sometido a un régimen armónico permanente, dentro de un rango fisiológicamente plausible de parámetros, en conjunto con los datos obtenidos de mediciones. Estas simulaciones se utilizan para construir una base reducida mediante el método POD (Proper Orthogonal Decomposition), sobre la cual se resuelve un problema de optimización que busca el mejor ajuste frente a mediciones de desplazamiento disponibles en planos. Con el método se pueden estimar campos de desplazamiento utilizando solamente desplazamientos definidos en una cantidad limitada de planos. Se construye una manifold numérica de 2048 simulaciones tridimensionales variando módulo de Young, razón de Poisson, permeabilidad y frecuencia de excitación; el análisis de valores singulares muestra que la mayor parte de la información se concentra en los primeros 50 modos, y que un modelo reducido con esta cantidad de modos resulta suficiente para describir la dinámica relevante. Con este esquema, los campos sintéticos se reconstruyen con errores relativos en norma 2 del orden de 0,0001 %, validando la consistencia interna del enfoque. En mediciones in vivo, el método alcanza errores relativos en norma 2 del orden de 15–20 % al comparar en la misma slice utilizada para la reconstrucción, y del orden de 27 % al interpolar entre slices. Adicionalmente, la incorporación de modos guiados por datos mediante OMP (Orthogonal Modes Pursuit) permite reducir aún más el error para un mismo número de modos combinados versus la POD tradicional, mostrando que el enfoque híbrido ROM (Reduced Order Model)–PBDW constituye una alternativa promisoria para la reconstrucción de campos mecánicos en elastografía cerebral.

Nonreciprocal propagation of spin waves in magnetic media is one of the central ingredients for information transport and signal processing in magnonics. In this context, the magnetic toroidal moment ($\boldtau$), originally introduced in the context of multiferroic systems and the magnetoelectric effect, can indicate configurations that break both spatial inversion and time-reversal symmetries and is therefore a quantity to describe and design nonreciprocal responses. However, its formulation within micromagnetism, such as the different definitions, the choice of origin, and its connection to magnon spectra, remains only partially understood. This thesis develops a micromagnetic framework for toroidal moments in confined magnetic structures and establishes their impact on spin-wave nonreciprocity. Different definitions of the toroidal moment are revisited and related in terms of continuous magnetization distributions, where volume and surface contributions to the toroidal moment are found. The role of the choice of origin and a possible connection between toroidal moments and the antisymmetric part of the spin-wave tensor are studied. The toroidal moment is evaluated for a broad class of magnetic systems and geometries, including ferromagnets with electric currents, conical–helical states in thin films and tubes, skyrmions, merons and bimerons in confined structures, and ferromagnetic systems with graded magnetization, revealing the conditions for the presence of a toroidal moment according to the magnetic ground state, some parameters, and geometry. Moreover, the thesis corroborates that a finite projection $\boldsymbol{\tau}\cdot\mathbf{k}\neq 0$ between the toroidal moment and the spin-wave wave vector provides a useful condition for nonreciprocal magnon propagation. This criterion is shown to agree with well-known mechanisms of nonreciprocity, such as current-driven Doppler shifts, graded magnetic profiles, Dzyaloshinskii–Moriya interaction, and curvature-induced symmetry breaking in curved geometries. Interestingly, from an optical perspective, the propagation of electromagnetic waves in a medium with a finite toroidal moment yields a dispersion relation containing the scalar product $\boldsymbol{\tau}\cdot\mathbf{k}$. Moreover, the dynamics of the toroidization are captured by a pair of equations of motion analogous to the Landau–Lifshitz–Gilbert equation, which couple the toroidization to the magnetization and to the monopolar magnetoelectric contribution. Altogether, these results consolidate the toroidal moment as a key parameter for designing nonreciprocal magnonic systems in novel magnetic media. Finally, the evolution from flat stripes to closed nanotubes is analyzed, revealing curvature-induced parity breaking, mode hybridization, and dispersion asymmetries in agreement with the associated toroidal moments.

En esta tesis se realiza el desarrollo de un modelo de Reinforcement Learning para optimización secuencial de portafolios, incorporando el riesgo mediante el Expected Shortfall. El modelo aprende políticas de decisiones sobre activos de un portafolio (comprar, vender o mantener) usando información de retornos de distintos tipos de activos (acciones, ETFs y criptomonedas) para distintas líneas temporales, junto con un umbral de decisión compuesta que se adapta de forma dinámica según la volatilidad de los retornos. Basándose en prueba y error, obteniendo como recompensa una métrica de retorno ajustado por riesgo, definida por el retorno marginal obtenido de un conjunto de movimientos respecto a una inversión libre de riesgo, ajustada por una métrica de riesgo explícita.El objetivo principal de la tesis es evaluar si un enfoque de Reinforcement Learning logra detectar patrones dinámicos de mercado y lograr generalizarlos para escenarios fuera de muestra. Comparándolo con otros modelos tanto financieros estadísticos, como modelos de machine learning supervisados y no supervisados. Los resultados muestran que, para enfoques más conservadores sobre el umbral, el modelo de Reinforcement Learning tiene un menor gap entre entrenamiento validación y testeo, siendo sus resultados más permanentes entre fases. Los enfoques más de rentabilidad pura, poseen mayor volatilidad entre fases de validación y testeo, teniendo un mayor riesgo que sus competidores y resultados similares.

Piscirickettsia salmonis is the etiological agent of Piscirickettsiosis, one of the most severe and detrimental diseases affecting the Chilean salmon farming industry. Currently, there are 26 vaccines with provisional registration available for prophylactic use in salmonids. However, none have proven effective in fully controlling epidemic outbreaks under field conditions. Several researchers have identified two predominant genogroups along the Chilean coast, designated as LF-89 and EM-90, which exhibit a variable spatiotemporal distribution in southern Chilean salmon farms in recent years. Recent evidence suggests that the biological mechanisms of both genogroups may interact synergistically during infection, indicating the potential for co-infections in contemporary farms. This scenario further complicates vaccine efficacy, as many are designed to target only a predominant genogroup. Thus, the need arises to identify alternative prophylactic methods to ensure and contribute to the sustainability of the Chilean salmon farming industry. A novel prophylactic approach proposed in this thesis involves the use of recombinant nanoproteins, also known as inclusion bodies, with antigenic functions against Piscirickettsiosis. These inclusion bodies are aggregates of soluble and insoluble proteins with amyloid characteristics, eliminating the need for encapsulation. They are scalable, lyophilizable, and highly stable under adverse temperature and pH conditions. This thesis aims to evaluate the ability of certain antigenic protein inclusion bodies from P. salmonis to induce an adaptive immune response in Atlantic salmon (Salmo salar) and their potential use as immunogens in a vaccine prototype. To achieve this objective, three antigenic sequences with chimeric characteristics from the LF-89 and EM-90 genogroups of P. salmonis were designed and produced as recombinant inclusion bodies in E. coli BL21(DE3). The prototypes were designated for commercial protection purposes as SkipZ, PulseJ, and HopQ. These prototypes were quantified and characterized using techniques such as Western blot. Their uptake and immunostimulatory activity were evaluated in salmon macrophages through in vitro assays. Subsequently, juvenile salmon were vaccinated, and the effects of the SkipZ and HopQ prototypes were analyzed in terms of gene expression (qPCR) and immunoglobulin production (ELISA). The three prototypes achieved production efficiencies of 57 mg/L for PulseJ, 40 mg/L for SkipZ, and 26,8 mg/L for HopQ. These prototypes were internalized by salmon macrophages (RTS-11) with uptake efficiencies of 5% for SkipZ, 26% for PulseJ, and 54% for HopQ. In vitro assays showed activation at both transcriptional and translational levels of molecules associated with antigen presentation and pro-inflammatory markers, with notable activation capacities observed for HopQ and SkipZ at 20 µg/mL and 5 µg/mL, respectively. In vivo assays revealed that both vaccine prototypes, administered at doses of 0.5 mg/kg of fish for SkipZ and 2 mg/kg of fish for HopQ, robustly activated the Th1 response, with a marked increase in IFN-γ and IL-12 expression. This strong upregulation might suppress CD8+ lymphocyte differentiation, potentially reducing the effectiveness of the cellular immune response in fully eliminating the pathogen. However, this effect may contribute to maintaining homeostasis in fish during the exacerbated inflammation induced by the vaccines. Moreover, no significant change was observed in the Th2 response, suggesting that the humoral response was not predominantly activated. In conclusion, the vaccine prototypes based on antigenic sequences of Piscirickettsia salmonis and produced as chimeric inclusion bodies demonstrated potent immunogenicity, capable of inducing a robust adaptive immune response in Atlantic salmon. The activation of the Th1 response by HopQ and SkipZ was particularly noteworthy. However, further studies are needed to evaluate their protective effects in challenge trials with the pathogen, including both genogroups, and to determine their long-term effectiveness.

This study introduces a novel measure for evaluating attribute relevance, specifically designed to accurately identify attributes that are intrinsically related to a phenomenon, while being sensitive to the asymmetry of those relationships and noise conditions. Traditional variable selection techniques, such as filter and wrapper methods, often fall short in capturing these complexities. Our methodology, grounded in decision trees but extendable to other machine learning models, was rigorously evaluated across various data scenarios. The results demonstrate that our measure effectively distinguishes relevant from irrelevant attributes and highlights how relevance is influenced by noise, providing a more nuanced understanding compared to established methods such as Pearson, Spearman, Kendall, MIC, MAS, MEV, GMIC, and 𝑃ℎ𝑖𝑘 This research underscores the importance of phenomenon-centric explainability, reproducibility, and robust attribute relevance evaluation in the development of predictive models. By enhancing both the interpretability and contextual accuracy of models, our approach not only supports more informed decision making but also contributes to a deeper understanding of the underlying mechanisms in diverse application domains, such as biomedical research, financial modeling, astronomy, and others.

A critical element in understanding voice production mechanisms is the characterization of vocal fold collision, which is widely considered a primary etiological factor in the development of common phonotraumatic lesions such as nodules and polyps. This paper describes the development of a transoral, dual-sensor intraglottal/subglottal pressure probe for the simultaneous measurement of vocal fold collision and subglottal pressures during phonation using two miniature sensors positioned 7.6 mm apart at the distal end of a rigid cannula. Proof-of-concept testing was performed using excised whole-mount and hemilarynx human tissue aerodynamically driven into self-sustained oscillation, with systematic variation of the superior–inferior positioning of the vocal fold collision sensor. In the hemilarynx experiment, signals from the pressure sensors were synchronized with an acoustic microphone, a tracheal-surface accelerometer, and two high-speed video cameras recording at 4000 frames per second for top–down and en face imaging of the superior and medial vocal fold surfaces, respectively. As expected, the intraglottal pressure signal exhibited an impulse-like peak when vocal fold contact occurred, followed by a broader peak associated with intraglottal pressure build-up during the de-contacting phase. As subglottal pressure was increased, the peak amplitude of the collision pressure increased and typically reached a value below that of the average subglottal pressure. Results provide important baseline vocal fold collision pressure data with which computational models of voice production can be developed and in vivo measurements can be referenced.