Spatially homogeneous solutions of the Vlasov-Nordstr\"om-Fokker-Planck system

The Vlasov-Nordstr\"{o}m-Fokker-Planck system describes the evolution of self-gravitating matter experiencing collisions with a fixed background of particles in the framework of a relativistic scalar theory of gravitation. We study the spatially-homogeneous system and prove global existence and uniqueness of solutions for the corresponding initial value problem in three momentum dimensions. Additionally, we study the long time asymptotic behavior of the system and prove that even in the absence of friction, solutions possess a non-trivial asymptotic profile. An exact formula for the long time limit of the particle density is derived in the ultra-relativistic case.Comment: 25 pages, 1 figure. Several changes from previous version. To appear in J. Diff. Eq

Contribution to the study of nonstationary aerodynamic forces in problems of interest for Micro-Air Vehicles

The main aim of this dissertation is the quantitative characterization of the contributions of individual fluid elements (vortices) to aerodynamic forces, explaining and quantifying the mechanisms by which both drag and lift are generated. For this purpose, a vorticity forces formulation was used to the two problems addressed in this thesis. Thus, a novel physical point of view of the flow dynamics is provided which is expected to be useful for the Micro-Air Vehicles (MAVs) design. Firstly, the well-known Magnus effect problem is studied. In this problem, the two-dimensional flow past a spinning cylinder is investigated numerically using a vorticity forces formulation with the aim of analyzing quantitatively the flow structures, and their evolutions, that contribute to the lift and drag forces on the cylinder. The Reynolds number considered, based on the cylinder diameter and steady free stream speed, is Re = 200, while the non-dimensional rotation rate (ratio of the surface speed and free stream speed) selected was α = 1 and 3. For α = 1 the wake behind the cylinder for the fully developed flow is oscillatory due to vortex shedding, and so are the lift and drag forces. For α = 3 the fully developed flow is steady with constant (high) lift and (low) drag. Each of these cases is considered in two different transient problems, one with angular acceleration of the cylinder and constant speed, and the other one with translating acceleration of the cylinder and constant rotation. We characterize quantitatively the contributions of individual fluid elements (vortices) to aerodynamic forces, explaining and quantifying the mechanisms by which the lift is generated in each case. In particular, for high rotation (when α = 3), we explain the relation between the mechanisms of vortex shedding suppression and those by which the lift is enhanced and the drag is almost suppressed when the fully developed flow is reached. On the other hand, the thrust efficiency of a two-dimensional flapping airfoil is studied computationally for a low Reynolds number via the same vortex force decomposition as the one cited previously. The auxiliary potentials that separate the total vortex force into lift and drag (or thrust) are obtained analytically by using an elliptic airfoil. With these auxiliary potentials, the added-mass components of the lift and drag (or thrust) coefficients are also obtained analytically for any heaving motion of the airfoil and for any value of the mean angle of attack α. The contributions of the leading- and trailing-edge vortices to the thrust during their down- and up-stroke evolutions, are computed quantitatively with this formulation for different dimensionless frequencies and heave amplitudes (St c and St a ) and for several values of α. Very different types of flows, periodic, quasi-periodic, and chaotic, described as St c , St a , and α, are varied. The optimum values of these parameters for maximum thrust efficiency are obtained and explained in terms of the interactions between the vortices and the forces exerted by them on the airfoil. As in previous numerical and experimental studies on flapping flight at low Reynolds numbers, the optimum thrust efficiency is reached for intermediate frequencies (St c slightly smaller than one) and a heave amplitude corresponding to an advance ratio close to unity. The optimal mean angle of attack found is zero. The corresponding flow is periodic, but it becomes chaotic and with smaller average thrust efficiency as |α| becomes slightly different from zero. Finally, some conclusions and some future work related to the MAVs design based on the vortex force decomposition to study some other interesting flight mechanisms are outlined

Diseño de un sistema de transporte de áridos desde tolva de alimentación hasta máquina cribadora

El presente proyecto tiene como objeto llevar a buen fin el diseño de un sistema de transporte de áridos desde una tolva de descarga hasta una máquina cribadora. Dicho sistema de manutención se diseñará en función de las condiciones que precise su emplazamiento y del material a transportar. El sistema de manutención consta de 3 tramos consecutivos. El primer tramo recibe el material desde una tolva de descarga y debe salvar horizontalmente 6 metros de longitud. El segundo, comunicado con el primero, se dispone verticalmente, salvando 3 metros de altura y el último tramo, conectado a su vez con el segundo, se dispondrá horizontalmente y tendrá 3 metros de longitud.Escuela Técnica Superior de Ingeniería IndustrialUniversidad Politécnica de Cartagen

A Neural Network-Based Distributional Constraint Learning Methodology for Mixed-Integer Stochastic Optimization

The use of machine learning methods helps to improve decision making in different fields. In particular, the idea of bridging predictions (machine learning models) and prescriptions (optimization problems) is gaining attention within the scientific community. One of the main ideas to address this trade-off is the so-called Constraint Learning (CL) methodology, where the structures of the machine learning model can be treated as a set of constraints to be embedded within the optimization problem, establishing the relationship between a direct decision variable $x$ and a response variable $y$. However, most CL approaches have focused on making point predictions for a certain variable, not taking into account the statistical and external uncertainty faced in the modeling process. In this paper, we extend the CL methodology to deal with uncertainty in the response variable $y$. The novel Distributional Constraint Learning (DCL) methodology makes use of a piece-wise linearizable neural network-based model to estimate the parameters of the conditional distribution of $y$ (dependent on decisions $x$ and contextual information), which can be embedded within mixed-integer optimization problems. In particular, we formulate a stochastic optimization problem by sampling random values from the estimated distribution by using a linear set of constraints. In this sense, DCL combines both the high predictive performance of the neural network method and the possibility of generating scenarios to account for uncertainty within a tractable optimization model. The behavior of the proposed methodology is tested in a real-world problem in the context of electricity systems, where a Virtual Power Plant seeks to optimize its operation, subject to different forms of uncertainty, and with price-responsive consumers

El circo social en la construcción de ciudadanía. Un estudio desde las competencias ciudadanas para una cultura democrática

Màster Oficial d'Intervencions Socials i Educatives, Facultat d'Educació, Universitat de Barcelona. Curs: 2021-2022. Tutora: Llena Berñe, AsunEsta investigación tiene como objetivo indagar en el papel del circo social para la construcción de ciudadanía a través del aprendizaje y práctica de las competencias ciudadanas. El circo social permite aprender circo y generar espacios de creación colectiva con la intención de promover el empoderamiento personal, grupal y comunitario para la mejora de la calidad de vida de los participantes y la sociedad que les envuelve. Esta práctica educativa y artística se muestra como un escenario ideal para aprender y poner en práctica competencias ciudadanas. Estas competencias se entienden como el conjunto de conocimientos, capacidades, habilidades y actitudes necesarias para dar respuesta los retos de una sociedad interconectada, con un crecimiento económico desigual y cada vez más diversa culturalmente

Erosional processes in rocky and sedimentary coasts

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A Neural Network-Based Distributional Constraint Learning Methodology for Mixed-Integer Stochastic Optimization

The use of machine learning methods helps to improve decision making in different fields. In particular, the idea of bridging predictions (machine learning models) and prescriptions (optimization problems) is gaining attention within the scientific community. One of the main ideas to address this trade-off is the so-called Constraint Learning (CL) methodology, where the structures of the machine learning model can be treated as a set of constraints to be embedded within the optimization problem, establishing therelationship between a direct decision variable x and a response variable y. However, most CL approaches have focused on making point predictions for a certain variable, not taking into account the statistical and external uncertainty faced in the modeling process. In this paper, we extend the CL methodology to deal with uncertainty in the response variable y. The novel Distributional Constraint Learning (DCL) methodology makes use of a piece-wise linearizable neural network-based model to estimate the parametersof the conditional distribution of y (dependent on decisions x and contextualinformation), which can be embedded within mixed-integer optimization problems. In particular, we formulate a stochastic optimization problem by sampling random values from the estimated distribution by using a linear set of constraints. In this sense, DCL combines both the high predictive performance of the neural network method and the possibility of generating scenarios to account for uncertainty within a tractable optimization model. The behavior of the proposed methodology is tested in a real-worldproblem in the context of electricity systems, where a Virtual Power Plant seeks to optimize its operation, subject to different forms of uncertainty, and with price-responsive consumers

Feasibility and reliability of a Physical Fitness test battery in individuals with Down Syndrome

Background: Down syndrome (DS) is a genetic disorder that occurs because of an abnormal division between cells that results in an extra copy of chromosome 21. Some studies show that physical exercise in people with DS increases some cognitive capacities, such as memory, and improves the quality of life. Aim: The main aim of this study was to perform an analysis of the reliability and feasibility of the SAMU-Disability Fitness (DISFIT) battery in adults with DS. Methods: A cross-sectional study with a test–retest design was performed in a maximum interval of 2 weeks in 37 subjects (11 women and 26 men) aged between 21 and 58 years old with DS. Eight field-based fitness tests were proposed to assess the physical fitness (PF) of adults with DS: Body Mass Index (BMI), Waist Circumference (WC), the Timed Up and Go test (TUG), the Deep Trunk Flexibility test (DTF), the Hand Grip test (HG), the Timed Stand Test (TST), the 30-s Sit-Up (SUP) and the 6-Min Walk Test (6MWT). Results: The intra-class correlation coefficient (ICC) in all the tests was good and high (>0.80), except for the 6MWT, whose reliability was fair. Conclusion: The SAMU-DISFIT battery is a reliable and feasible physical fitness battery which has been created with the purpose of establishing tests which measure the four basic components of PF (flexibility, cardiorespiratory fitness, musculoskeletal fitness and motor fitness) in adults with D

International inequality in energy intensity levels and the role of production composition and energy efficiency : an analysis of OECD countries

This paper analyses the inequality of energy intensity levels between OECD countries, its causes and evolution. The paper develops a methodology which allows the inequality in energy consumption per capita to be decomposed into explanatory factors. It also analyses the contribution of different groups of countries to this inequality. The results show that although differences in affluence are the most significant factor in explaining inequality in energy consumption per capita, the inequality in energy intensity levels plays a prominent role in reducing the inequality in energy consumption per capita over the analysed period. The paper also develops a methodology which determines the importance of different production structures and energy efficiency of productive sectors in the differences in energy use per unit of GDP between the countries analysed. The results show that sector specialisation becomes increasingly important in explaining the inequality of energy intensity, while there is a significant trend towards the convergence of energy efficiency between countries sector by sector. This trend would explain the decreasing weight of energy intensity as an explanatory factor of the inequalities in energy consumption per capita. © 2010 Elsevier B.V