New effective theories of gravitation and their phenomenological consequences

The objective of this Thesis is to explore Poincaré Gauge theories of gravity and expose some contributions to this field, which are detailed below. Moreover, a novel ultraviolet non-local extension of this theory shall be provided, and it will be shown that it can be ghost- and singularity-free at the linear level. First, we introduce some fundamentals of differential geometry, base of any gravitational theory. We then establish that the affine structure and the metric of the spacetime are not generally related, and that there is no physical reason to impose a certain affine connection to the gravitational theory. We review the importance of gauge symmetries in Physics and construct the quadratic Lagrangian of Poincaré Gauge gravity by requiring that the gravitational theorymust be invariant under local Poincaré transformations. We study the stability of the quadratic Poincaré Gauge Lagrangian, and prove that only the two scalar degrees of freedom (one scalar and one pseudo-scalar) can propagate without introducing pathologies. We provide extensive details on the scalar, pseudo-scalar, and bi-scalar theories. Moreover, we suggest how to extend the quadratic Poincaré Gauge Lagrangian so that more modes can propagate safely. We then proceed to explore some interesting phenomenology of Poincaré Gauge theories. Herein, we calculate how fermionic particles move in spacetimes endowed with a nonsymmetric connection at first order in the WKB approximation. Afterwards, we use this result in a particular black-hole solution of Poincaré Gauge gravity, showing that measurable differences between the trajectories of a fermion and a boson can be observed. Motivated by this fact, we studied the singularity theorems in theories with torsion, to see if this non-geodesical behaviour can lead to the avoidance of singularities. Nevertheless, we prove that this is not possible provided that the conditions for the appearance of black holes of any co-dimension are met. In order to see which kind Black Hole solutions we can expect in Poincaré Gauge theories, we study Birkhoff and no-hair theorems under physically relevant conditions. Finally, we propose an ultraviolet extension of Poincaré Gauge theories by introducing non-local (infinite derivatives) terms into the action, which can ameliorate the singular behaviour at large energies. We find solutions of this theory at the linear level, and prove that such solutions are ghost- and singularity-free. We also find new features that are not present in metric Infinite Derivative Gravity

Vector stability in quadratic metric-affine theories

In this work we study the stability of the four vector irreducible pieces of the torsion and the nonmetricity tensors in the general quadratic metric-affine Lagrangian in 4 dimensions. This highly constrains the theory reducing the parameter space of the quadratic curvature part from 16 to 5 parameters. We also study the sub-case of Weyl-Cartan gravity, proving that the stability of the vector sector completely fixes the dynamics of the full Lagrangian to just an Einstein-Proca theory or pure General Relativity.Comment: 21 pages, no figures, no table

Fitting Biochars and Activated Carbons from Residues of the Olive Oil Industry as Supports of FeCatalysts for the Heterogeneous Fenton-Like Treatment of Simulated Olive Mill Wastewater

Bruno Esteves is grateful to FCT for financial support through the PhD grant (SFRH/BD/129235/2017), with financing from National and the European Social Funds through the Human Capital Operational Programme (POCH). Sergio Morales-Torres acknowledges the financial support from the University of Granada (Reincorporación Plan Propio).A series of biochars and activated carbons (ACs) was prepared combining carbonization and physical or chemical activation of cheap and abundant residues of the olive oil industry. These materials were used as Fe-support to develop low-cost catalysts for the heterogeneous Fenton-like oxidation of simulated olive mill wastewater (OMW), the highly pollutant effluent generated by this agroindustry. Commercial ACs were also used as reference. All catalysts prepared were extensively characterized and results related with their performances in the catalytic wet peroxide oxidation (CWPO). Results showed a linear relationship of the textural properties of the catalysts with the adsorptive and catalytic performance, as well as the preferential adsorption and degradation of some phenolic compounds (caffeic and gallic acids) by specific interactions with the catalysts’ surface. Despite the best performance of catalysts developed using commercial supports, those prepared from agro-industrial residues present some advantages, including a smaller catalyst deactivation by iron leaching. CWPO results show that catalysts from physically activated olive stones are the most promising materials, reaching total organic carbon and toxicity reductions of 35% and 60%, respectively, as well an efficient use of H2O2, comparable with those obtained using commercial supports. This approach showed that the optimized treatment of this type of residues will allow their integration in the circular economic process of the olive oil production.Laboratory for Process Engineering, Environment, Biotechnology and Energy-LEPABE - FCT/MCTES (PIDDAC) - European Regional Development Funds (ERDF) through North Portugal Regional Operational Programme (NORTE 2020) UIDB/00511/2020 NORTE-01-0247-FEDER-39789Spanish Project from ERDF/Ministry of Science, Innovation and Universities-State Research Agency RTI2018-099224-B-I0

Revisiting the Stability of Quadratic Poincar\'e Gauge Gravity

Poincar\'e gauge theories provide an approach to gravity based on the gauging of the Poincar\'e group, whose homogeneous part generates curvature while the translational sector gives rise to torsion. In this note we revisit the stability of the widely studied quadratic theories within this framework. We analyse the presence of ghosts without fixing any background by obtaining the relevant interactions in an exact post-Riemannian expansion. We find that the axial sector of the theory exhibits ghostly couplings to the graviton sector that render the theory unstable. Remarkably, imposing the absence of these pathological couplings results in a theory where either the axial sector or the torsion trace becomes a ghost. We conclude that imposing ghost-freedom generically leads to a non-dynamical torsion. We analyse however two special choices of parameters that allow a dynamical scalar in the torsion and obtain the corresponding effective action where the dynamics of the scalar is apparent. These special cases are shown to be equivalent to a generalised Brans-Dicke theory and a Holst Lagrangian with a dynamical Barbero-Immirzi pseudoscalar field respectively. The two sectors can co-exist giving a bi-scalar theory. Finally, we discuss how the ghost nature of the vector sector can be avoided by including additional dimension four operators.Comment: 25 pages, 1 figure. More insight on the bi-scalar theory has been added, including its possible extensions and the coupling with fermions. A clarifying footnote on the Holst term has been introduced. Extended discussion. It matches the version published in EPJ

Junction conditions in infinite derivative gravity

The junction conditions for the infinite derivative gravity theory ${R{+}RF(\Box)R}$ are derived under the assumption that the conditions can be imposed by avoiding the `ill-defined expressions' in the theory of distributions term by term in infinite summations. We find that the junction conditions of such non-local theories are much more restrictive than in local theories, since the conditions comprise an infinite number of equations for the Ricci scalar. These conditions can constrain the geometry far beyond the matching hypersurface. Furthermore, we derive the junction field equations which are satisfied by the energy-momentum on the hypersurface. It turns out that the theory still allows some matter content on the hypersurface (without external flux and external tension), but with a traceless energy-momentum tensor. We also discuss the proper matching condition where no matter is concentrated on the hypersurface. Finally, we explore the possible applications and consequences of our results to the braneworld scenarios and star models. Particularly, we find that the internal tension is given purely by the trace of the energy-momentum tensor of the matter confined to the brane. Consequences of the junction conditions are illustrated on two simple examples of static and collapsing stars. It is demonstrated that even without solving the field equations the geometry on one side of the hypersurface can be determined to a great extent by the geometry on the other side if the Ricci scalar is analytic. We further show that some usual star models in the general relativity are no longer solutions of the infinite derivative gravity.Comment: 18 pages, 2 figure

New nonsingular cosmological solution of nonlocal gravity

We present a new bouncing cosmological solution of the non-local theory known as infinite derivative gravity, which goes beyond the recursive ansatz, ${\Box R = r_1 R +r_2}$. The non-local field equations are evaluated using the spectral decomposition with respect to the eigenfunctions of the wave operator. The energy-momentum tensor computed for this geometry turns out to be much more sensitive to the choice of the non-local form-factor, since it depends on the value of the function on a continuous infinite interval. We show that this stronger dependence on the form-factor allows us to source the geometry by the perfect fluid with the non-negative energy density satisfying the strong energy condition. We show that this bouncing behaviour is not possible in the local theories of gravity such as in general relativity or $R+R^2$ gravity sourced by a fluid which meets the non-negative energy and strong energy conditions.Comment: 12 page

Evaluación de la tendencia al esfuerzo cognitivo

Se analizan las características psicométricas de la Escala de Necesidad Cognitiva, traducida y adaptada al español a partir de la original en lengua inglesa de Petty y Cacioppo (1986). El constructo Necesidad de Cognición se requiere a una motivación intrínseca para implicarse y disfrutar con tareas que suponen esfuerzo cognitivo. La escala fue administrada a un total de 232 sujetos, obteniendo fiabilidad y validez aceptables y una estructura jactoriai que descubre cuatro dimensiones: anticipación, resolucion de problemas, activación e implicación personai

Métodos alternativos de solución de conflictos

Cada individuo presenta necesidades y puntos de vista diferentes, por lo que es inevitable que surjan situaciones conflictivas en sus relaciones en sociedad. Para algunas personas estas situaciones pueden ser consideradas como algo negativo, creándoles un estado de tensión y ansiedad; mientras que para otras, su adecuada resolución puede significar una manera eficaz de prosperar como ser humano en su vida personal y profesional. Los conflictos han sido resueltos tradicionalmente en vía judicial, siendo un juez el competente para la resolución de los mismos mediante sentencia. Los ADR como métodos alternativos para la resolución de conflictos, surgen como consecuencia de las limitaciones que presenta el sistema judicial.En el caso de España señalar que la asimilación de las técnicas de ADR no ha evolucionado como en otros países. Entre los distintos tipos de ADR podemos destacar: la mediación, la negociación, la conciliación y el arbitraje.Grado en Criminologí

Modelos predictivos basados en deep learning para datos temporales masivos

Programa de Doctorado en Biotecnología, Ingeniería y Tecnología QuímicaLínea de Investigación: Ingeniería, Ciencia de Datos y BioinformáticaClave Programa: DBICódigo Línea: 111El avance en el mundo del hardware ha revolucionado el campo de la inteligencia artificial, abriendo nuevos frentes y áreas que hasta hoy estaban limitadas. El área del deep learning es quizás una de las mas afectadas por este avance, ya que estos modelos requieren de una gran capacidad de computación debido al número de operaciones y complejidad de las mismas, motivo por el cual habían caído en desuso hasta los últimos años. Esta Tesis Doctoral ha sido presentada mediante la modalidad de compendio de publicaciones, con un total de diez aportaciones científicas en Congresos Internacionales y revistas con alto índice de impacto en el Journal of Citation Reports (JCR). En ella se recoge una investigación orientada al estudio, análisis y desarrollo de las arquitecturas deep learning mas extendidas en la literatura para la predicción de series temporales, principalmente de tipo energético, como son la demanda eléctrica y la generación de energía solar. Además, se ha centrado gran parte de la investigación en la optimización de estos modelos, tarea primordial para la obtención de un modelo predictivo fiable. En una primera fase, la tesis se centra en el desarrollo de modelos predictivos basados en deep learning para la predicción de series temporales aplicadas a dos fuentes de datos reales. En primer lugar se diseñó una metodología que permitía realizar la predicción multipaso de un modelo Feed-Forward, cuyos resultados fueron publicados en el International Work-Conference on the Interplay Between Natural and Artificial Computation (IWINAC). Esta misma metodología se aplicó y comparó con otros modelos clásicos, implementados de manera distribuida, cuyos resultados fueron publicados en el 14th International Work-Conference on Artificial Neural Networks (IWANN). Fruto de la diferencia en tiempo de computación y escalabilidad del método de deep learning con los otros modelos comparados, se diseñó una versión distribuida, cuyos resultados fueron publicados en dos revistas indexadas con categoría Q1, como son Integrated Computer-Aided Engineering e Information Sciences. Todas estas aportaciones fueron probadas utilizando un conjunto de datos de demanda eléctrica en España. De forma paralela, y con el objetivo de comprobar la generalidad de la metodología, se aplicó el mismo enfoque sobre un conjunto de datos correspondiente a la generación de energía solar en Australia en dos versiones: univariante, cuyos resultados se publicaron en International on Soft Computing Models in Industrial and Environment Applications (SOCO), y la versión multivariante, que fué publicada en la revista Expert Systems, indexada con categoría Q2. A pesar de los buenos resultados obtenidos, la estrategia de optimización de los modelos no era óptima para entornos big data debido a su carácter exhaustivo y al coste computacional que conllevaba. Motivado por esto, la segunda fase de la Tesis Doctoral se basó en la optimización de los modelos deep learning. Se diseñó una estrategia de búsqueda aleatoria aplicada a la metodología propuesta en la primera fase, cuyos resultados fueron publicados en el IWANN. Posteriormente, se centró la atención en modelos de optimización basado en heurísticas, donde se desarrolló un algoritmo genético para optimizar el modelo feed-forward. Los resultados de esta investigación se presentaron en la revista Applied Sciences, indexada con categoría Q2. Además, e influenciado por la situación pandémica del 2020, se decidió diseñar e implementar una heurística basada en el modelo de propagación de la COVID-19. Esta estrategia de optimización se integró con una red Long-Short-Term-Memory, ofreciendo resultados altamente competitivos que fueron publicados en la revista Big Data, indexada en el JCR con categoría Q1. Para finalizar el trabajo de tesis, toda la información y conocimientos adquiridos fueron recopilados en un artículo a modo de survey, que fue publicado en la revista indexada con categoría Q1 Big Data.Universidad Pablo de Olavide de Sevilla. Departamento de Deporte e Informátic