Antiferromagnetic Ising model in an imaginary magnetic field

We study the two-dimensional antiferromagnetic Ising model with a purely imaginary magnetic field, which can be thought of as a toy model for the usual $\theta$ physics. Our motivation is to have a benchmark calculation in a system which suffers from a strong sign problem, so that our results can be used to test Monte Carlo methods developed to tackle such problems. We analyze here this model by means of analytical techniques, computing exactly the first eight cumulants of the expansion of the effective Hamiltonian in powers of the inverse temperature, and calculating physical observables for a large number of degrees of freedom with the help of standard multi-precision algorithms. We report accurate results for the free energy density, internal energy, standard and staggered magnetization, and the position and nature of the critical line, which confirm the mean-field qualitative picture, and which should be quantitatively reliable, at least in the high-temperature regime, including the entire critical line

Massive Schwinger model at finite θ\theta

Using the approach developed in [V. Azcoiti, G. Di Carlo, A. Galante, V. Laliena, \textit{Phys. Lett.} \textbf{B563}, (2003) 117], we are able to reconstruct the behavior of the massive 1-flavor Schwinger model with a $\theta$ term and a quantized topological charge. We calculate the full dependence of the order parameter with $\theta$. Our results at $\theta = \pi$ are compatible with Coleman's conjecture on the phase diagram of this model.Comment: 8 pages, 8 figure

Solar irradiance forecasting using neural networks

Accurate solar irradiance forecasting is essential for minimizing operational costs of solar photovoltaic (PV) generation as it is commonly used to predict the power output. This thesis presents and compares three different machine learning approaches of solar irradiance forecasting: Random Forest (RF), Feedforward Neural Networks (FNNs) and Long Short-Term Memory (LSTM) networks. Each model was tested on two different forecasts: the next hour average and the hourly day-ahead averages. The machine learning algorithms were trained and tested on data from a weather station located at Tampere University (TAU) in Tampere, Finland. Data were preprocessed before training the algorithms and the relevant features were selected. Moreover, Grid Search and Random Search techniques were used along with multiple train and validation splits to find the optimal hyperparameters for each machine learning algorithm. Persistence model is set as a baseline model for comparison while RMSE and MAE are used to quantify the prediction error. For the next hour forecast, LSTM achieved the highest accuracy in terms of RMSE (76.14 W/m2 ), 2.1% and 1.1% better than RF and FNN respectively. Instead, FNN generally produced the best results in the day-ahead forecast. In all models, the prediction error increases as the forecast horizon increases until it stabilizes at 10 hours approximately. Further, the error keeps increasing but slower. Besides, the next hour forecast models were able to predict considerably better the next hour solar irradiance than the day-ahead forecast modelsOutgoin

Non-perturbative physics in lattice gauge theories

A few decades have passed since quantum chromodynamics (QCD) was established as the theory describing strong interactions. It is broadly accepted as one of the most successful theories in modern physics, and it has been extensively tested, both from the theoretical and the experimental perspectives.At high energies, QCD is asymptotically free, which means that its fundamental constituents, quarks and gluons, interact with a strength that decreases as the energy scale reaches higher values. In this regime, it is feasible to use perturbation theory to resolve short distance interactions. On the other hand, for not-so-high energies, the strong interaction cannot be reduced to a converging series of Feynman diagrams. In fact, one of the characteristic properties of QCD is the so-called color-confinement. In this purely non-perturbative regime, there are few techniques that can analyze the theory successfully. Probably the most well-established of them is lattice QCD. Since the foundational work of Wilson in 1974, the success of the lattice approach has been growing consistently over time. Many milestones have already been reached, including precise simulations that account for the effects of virtual quark loops, the determination of the light hadron spectrum with fully controlled systematics or, more recently, the computation of the isospin splittings with great agreement with the experimental data.For the above reasons, QCD is believed to be the correct theory describing strong interactions, both for high and low energies, and lattice QCD is recognized by the community as a trustworthy ab initio approach that has an useful interaction with experiment, paraphrasing Wilson. However, there are some fundamental topics that still constitute open questions. At least two problems share this status: the behavior of matter at finite baryonic density and the studies involving topological effects in QCD. The main difficulty behind the modest progress achieved in both areas is the same: the action of the theory is complex, and there is no known reformulation that can avoid the appearance of a severe sign problem (SSP).In this context, the main part of this thesis has been devoted to study models which suffer from a SSP, such as the two-dimensional Ising model within an imaginary magnetic field or the massive 1-flavor Schwinger model with a theta term. In the first case, we study the well-known model by means of analytical techniques, exploring a region of the parameter space somewhat unattended by the literature, possibly due to the difficulty of applying either analytical or numerical techniques. Secondly, and with the aim of engaging with QCD-like systems with a theta term and to develop further the methods dealing with the SSP, we have studied the massive 1-flavor Schwinger model with a $\theta$ term, which corresponds to QED in $1+1$ dimensions, and is in fact broadly used as its toy model. Moreover, defining the topological charge on the lattice is almost trivial in this model, in contrast with any of the usual definitions of this observable in lattice QCD, which are much more involved. As a byproduct of this line of work, and driven by the necessity of optimizing further our previous algorithms, we have also analysed the 2-flavor version of the Schwinger model. In this case, we have bypassed the computation of the full fermionic determinant by following an approach based on the use of pseudofermions.Finally, beyond the study of systems afflicted by a SSP, another topic within lattice QCD has been treated during the development of this thesis: the strong running coupling alpha_S. Its dependence with the momentum transfer, which encodes the underlying interactions of quarks and gluons in the QCD framework, constitutes a very active field of research, that includes a large variety of approaches. At large momenta, where perturbative QCD can be applied, both experimental and theoretical methods try to provide the most accurate approximation. In this context, lattice-based strategies have been capable of delivering results both in the infrared region and in the high energy regime, where in fact they provide the most precise determination of the coupling constant. Our work can be framed precisely into these approaches that come from lattice QCD, and it relies upon a ghost-gluon vertex computation.<br /

Modelo de madurez de BizDevOps

Programa Oficial de Doutoramento en Computación . 5009V01[Resumen] DevOps se ha impuesto en el sector de las Tecnologías de la Información (TI) como una aproximación eficaz a la interacción entre las funciones de desarrollo y operaciones. Recientemente, esta aproximación se ha ampliado a la interacción con las funciones de negocio, dando lugar al término BizDevOps. Aunque existen multitud de propuestas y herramientas que dan soporte a BizDevOps desde un punto de vista técnico, no se ha avanzado significativamente en aspectos de gestión, como la evaluación de las prácticas y procesos implicados en el área. En esta tesis proponemos un Modelo de Madurez de procesos para BizDevOps: MMBDO, basado en estándares internacionales de relevancia del sector TI.[Resumo] DevOps estableceuse no sector das tecnoloxías da información (TI) como un enfoque eficaz para a interacción entre o desenvolvemento e as funcións operativas. Recentemente, este enfoque estendeuse á interacción con funcións comerciais, dando lugar ao termo BizDevOps. Aínda que hai moitas propostas e ferramentas que apoian BizDevOps desde o punto de vista técnico, non se produciron avances significativos en aspectos de xestión, como a avaliación das prácticas e procesos implicados na área. Nesta tese propoñemos un Modelo de Madurez de procesos para BizDevOps: MMBDO, baseado nos estándares internacionais relevantes para o sector das TIC.[Abstract] DevOps has emerged in the Information Technologies (IT) sector as an effective approach to the interaction between development and operation functions. Recently, this approach has been extended to the interaction with business functions, generating the term BizDevOps. Although there are many proposals and tools supporting BizDevOps from a technical viewpoint, there has been no significant progress in management aspects, such as the evaluation of practices and processes involved in the area. In this thesis we propose a process Maturity Model for BizDevOps: MMBDO, based on relevant international standards of the IT sector

A new generation of vectors with increased induction ratios by overimposing a second regulatory level by attenuation

A major drawback of regulated gene expression from vectors bearing strong promoters is the associated high basal expression level. Simple regulatory systems have an intrinsic limitation in the range of induction, and attempts to mutate promoters to reduce basal expression usually result in concomitant reduction of induced levels. We have explored the possibility of reducing basal levels of gene expression while keeping induced levels intact by incorporating an additional regulatory circuit controlling a different step of the expression process. We have integrated the nasFEDCBA transcriptional attenuation system of Klebsiella oxytoca into a cascade expression circuit based on different regulatory elements of Pseudomonas putida, and also into a system based on the tac promoter, to expand their regulatory capacity. Basal expression from the promoters of these circuits was reduced by more than 10-fold by the nasF attenuator sequence while keeping the induced levels intact in the presence of the antiterminator protein, thus increasing the induction ratio by up to 1700-fold. In addition, using different combinations of regulatory elements and inducing conditions, we were able to obtain a broad range of expression levels. These vectors and the concept of their design will be very useful in regulating overproduction of heterologous proteins both at laboratory and industrial scales

Solar Irradiance Forecasting Using Neural Networks

Accurate solar irradiance forecasting is essential for minimizing operational costs of solar photovoltaic (PV) generation as it is commonly used to predict the power output. This thesis presents and compares three different machine learning approaches of solar irradiance forecasting: Random Forest (RF), Feedforward Neural Networks (FNNs) and Long Short-Term Memory (LSTM) networks. Each model was tested on two different forecasts: the next hour average and the hourly day-ahead averages. The machine learning algorithms were trained and tested on data from a weather station located at Tampere University (TAU) in Tampere, Finland. Data were preprocessed before training the algorithms and the relevant features were selected. Moreover, Grid Search and Random Search techniques were used along with multiple train and validation splits to find the optimal hyperparameters for each machine learning algorithm. Persistence model is set as a baseline model for comparison while RMSE and MAE are used to quantify the prediction error. For the next hour forecast, LSTM achieved the highest accuracy in terms of RMSE (76.14 W/m2), 2.1% and 1.1% better than RF and FNN respectively. Instead, FNN generally produced the best results in the day-ahead forecast. In all models, the prediction error increases as the forecast horizon increases until it stabilizes at 10 hours approximately. Further, the error keeps increasing but slower. Besides, the next hour forecast models were able to predict considerably better the next hour solar irradiance than the day-ahead forecast models

Engineered Salmonella allows real-time heterologous gene expression monitoring within infected zebrafish embryos

Short communication.Microbial host–pathogen interactions have been traditionally well studied at genetic and physiological levels, but cell-resolution analyses have been particularly scarce. This has been especially remarkable for intracellular parasites for two major reasons: first, the inherent loss of bacteria traceability once infects its hosts; second and more important, the limited availability of genetic tools that allow a tight regulated expression of bacterial virulence genes once inside the host tissues. Here we present novel data supporting the use of zebrafish embryos to monitor Salmonella enterica serovar Thyphimurium infection. Intravenous infection of Salmonella can be easily monitored using in vivo fluorescence that allows the visualization of free-swimming bacteria through the circulatory system. Moreover, we have engineered Salmonella to voluntarily activate heterologous gene expression at any point during infection once inside the zebrafish macrophages using a salicylate-based expression system. This approach allows real-time cell-resolution in vivo monitoring of the infection. All together, this approach paves the road to cell-based resolution experiments that would be harder to mimic in other vertebrate infection models.his work was funded by grants BFU2010-14839, CSD2007-00008, CSD2007-00005 and Proyectos de Excelencia CVI-3488 and P07-CVI-02518 from the Spanish and Andalusian Governments, respectively. Jose Luis Royo holds a JAE DOC contract from the Spanish National Research Council (CSIC).Peer reviewe

El uso de la robótica como herramienta para fomentar el pensamiento computacional: Un estudio cuasi-experimental

El presente trabajo incluye un estudio cuasi-experimental realizado con 22 alumnos de 2º de Educación Infantil. Dicha investigación gira entorno a averiguar si la robótica educativa es una buena herramienta para fomentar el pensamiento computacional en los infantes. Para llevar a cabo la investigación se ha realizado una prueba pretest, una intervención y una prueba postest a un grupo control y un grupo experimental. Posteriormente se han recogido los datos pertinentes en base a las dimensiones preestablecidas para el pretest y postest y se han analizado los resultados a través de pruebas no paramétricas como: U de Mann-Whitney y W de Wilcoxon. <br /

Desarrollo de un algoritmo de simulación geométrico para modelos de gauge acoplados a un campo de Higgs

En este trabajo se plantea como objetivo la implementación de un algoritmo de simulación geométrico para el modelo Z(2) gauge-Higgs. Tras una exposición de los resultados relevantes en el campo, se introduce el modelo de estudio y se prueba su dualidad con el modelo de Ising con campo magnético en dimensión dos. Posteriormente se describe en detalle el funcionamiento de los algoritmos implementados y se presentan los resultados obtenidos por estos, incluyendo una comparativa de la eficiencia computacional de cada uno. Adicionalmente, se estudia el modelo de Ising en su sector antiferromagnético, corroborando la existencia de una línea de transición que persiste para valores del campo no nulos, a diferencia del caso ferromagnético