Banco de la República - actividad cultural

    Helia: revista quincenal ilustrada - N. 7 y 8

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    Seismological characterization of the Theistareykir geothermal field (Iceland)

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    Abstract: In 2017 the National Power Company in Iceland, Landsvirkjun, started the operation of a geothermal power plant in Theistareykir (Northeastern Iceland). The plant’s operation requires extraction, circulation, and injection of the geothermal fluids to produce energy. These processes depend on the existing fracture network of the reservoir. Therefore, geothermal energy exploitation requires knowledge of underground structures to identify potential fluid flow pathways. These are, in many cases, evidenced by the local seismicity. In this context, the GFZ German Research Center for Geosciences and Landsvirkjun chose this site for deploying a dense network of fifteen seismic broadband stations to monitor and characterize the field’s seismicity. The data coming from this very dense network allows us to implement and test an optimized processing scheme to perform a detailed analysis of the local seismicity. This study’s primary goal is to implement an efficient and reliable scheme to characterize the local seismicity of the Theistareykir geothermal field using the collected high-resolution seismic data from the dense network. I used several traditional earthquake seismology methods to detect, analyze, classify, and localize, repeating microseismic events. I first used a recursive STA/LTA algorithm to detect the local seismicity between January 1, 2018, until June 30, 2018. Using the detections, I manually reviewed and picked P- and Sphase arrival times. After an initial non-linear localization, I performed a correlation clustering analysis and identified two events with a high degree of waveform similarity. I corrected the picked phase arrivals using the cross-correlation coefficients of the events to a master trace. These events were relocated to improve their relative locations. Events of both clusters will be used in future studies for template-matching to detect and pick additional events within this period. The methodology applied here is meant as a guide to process upcoming seismic data of the geothermal field and efficiently perform a full seismological characterization using more massive datasets

    Mefistófeles: semanario ilustrado de crítica social y política - N. 92

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    Chapinero: revista quincenal ilustrada - N. 4

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    La Renovación: diario político - N. 5

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    Statistical Methods for Campylobacter Outbreak Detection using Genomics and Epidemiological Data

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    Abstract: Campylobacter infections are the main bacterial cause of gastroenteritis in the UK, causing an estimated 500 thousand cases per year. Health authorities investigate outbreaks to identify the source, control the spread and understand the cause. Outbreak detection mechanisms are potentially improved by the increasing availability of whole-genome sequence alongside other epidemiological data. However, techniques mixing genomics and other epidemiological factors are still underdeveloped. This project aims to develop and apply outbreak detection methods using surveillance data collected from two regions in the UK. The approaches proposed in this thesis are based on an existing spatialtemporal Bayesian hierarchical model, where cases are labelled as potential outbreaks if they comprise an elevated number of cases compared to the expected sporadic count. The model is adjusted to include genetic data using Gaussian random fields, exploiting the capacity of whole-genome sequencing to discriminate closely related isolates. Moreover, a Markov Chain Monte Carlo algorithm is implemented to obtain the posterior distribution of the model parameters. In particular, a sampling strategy is proposed to improve the convergence of the chain for the parameters describing the Gaussian random field. The project dataset is analysed using a spatial-temporal, a spatial-genetic and a temporalgenetic version of the model, where each version explores different types of outbreaks. The proposed approach demonstrates how to organise genetic sequences into a highdimensional structure and incorporate them into a Bayesian framework. Also, the MCMC sampling algorithm improves the mixing of the chain to estimate the posterior distribution of the model parameters. Finally, all model versions provide the probability that each reported infection is part of a potential outbreak. Comparing the potential outbreaks found by each model provides insights to estimate the real outbreaks. It also identifies cases that are potentially part of a diffuse real outbreak hard to detect by existing approaches. Despite the capability of the model, it requires predefined outbreak sizes and therefore is not flexible at capturing many shapes. Autocorrelated models are a potential improvement to be explored. Resumen: Las infecciones por Campylobacter son la principal causa bacteriana de gastroenteritis en el Reino Unido, provocando un estimado de 500 mil casos por año. Las autoridades sanitarias investigan los brotes para identificar la fuente, controlar la propagación y comprender la causa. Los mecanismos de detección de brotes se mejoran potencialmente con la creciente disponibilidad de datos del genoma completo de las bacterias junto con otros datos epidemiológicos. Sin embargo, las técnicas que combinan genética y otros factores epidemiológicos aún están poco desarrolladas. Este proyecto tiene como objetivo desarrollar y aplicar métodos de detección de brotes utilizando datos de vigilancia recopilados en dos regiones del Reino Unido. Los enfoques propuestos en esta tesis se basan en un modelo jerárquico bayesiano espacio-temporal existente, donde los casos se etiquetan como brotes potenciales si presentan un número elevado de casos mayor al recuento esporádico esperado. El modelo se ajusta para incluir datos genéticos utilizando campos aleatorios Gaussianos, aprovechando la capacidad del genoma completo para discriminar casos estrechamente relacionados. Además, se implementa usando algoritmos de Markov Chain Monte Carlo para obtener la distribución posterior de los parámetros del modelo. En particular, se propone una estrategia de muestreo para mejorar la convergencia de la cadena de Markov para los parámetros del campo Gaussiano. El conjunto de datos del proyecto se analiza utilizando una versión espacial temporal, una espacial-genética y una temporal-genética del modelo, donde cada versión explora diferentes tipos de brotes. El enfoque propuesto demuestra cómo organizar secuencias genéticas en una estructura de múltiples dimensiones e incorporarlas en un marco bayesiano. Además, el algoritmo de muestreo MCMC mejora la mezcla de la cadena para estimar la distribución posterior de los parámetros del modelo. Por último, todas las versiones del modelo estiman la probabilidad de que cada infección sea parte de un posible brote. La comparación entre los posibles brotes encontrados por cada modelo proporciona información para estimar cuáles son los brotes reales. También identifica casos que son potencialmente parte de un brote real difícil de detectar por los enfoques existentes. A pesar de las múltiples ventajas del modelo, este requiere tamaños de brotes predefinidos y, por lo tanto, no es flexible para capturar brotes de formas irregulares. Los modelos autocorrelacionados proveen una mejora potencial para el modelo que debe explorarse

    Impact of dimensionality on nowcasting seasonal influenza with environmental factors

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    Abstract: Seasonal influenza is an infectious disease of multi-causal etiology and a major cause of mortality worldwide that has been associated with environmental factors. In the attempt to model and predict future outbreaks of seasonal influenza with multiple environmental factors, we face the challenge of increased dimensionality that makes the models more complex and unstable. In this paper, we propose a nowcasting and forecasting framework that compares the theoretical approaches of Single Environmental Factor and Multiple Environmental Factors. We introduce seven solutions to minimize the weaknesses associated with the increased dimensionality when predicting seasonal influenza activity levels using multiple environmental factors as external proxies. Our work provides evidence that using dimensionality reduction techniques as a strategy to combine multiple datasets improves seasonal influenza forecasting without the penalization of increased dimensionality

    Rear and Arraigo: Philosophical keys of the risk resettlement and its resistances in Bogota = Desgarre y arraigo: Claves filosóficas del reasentamiento por riesgos y sus resistencias en Bogotá

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    Resumen: Ensayo que pretende analizar, desde la disertación filosófica, los acontecimientos que se desarrollan en torno a la aplicación de la política de asignación de riesgos y reasentamiento de población en Bogotá y verificar la calidad de los mismos como manifestación de confrontaciones entre el poder hegemónico ejercido a través del Estado y el arraigo como expresión de fuerzas vivas de la heterogeneidad social, resaltando de estas últimas su contenido, como información fundamental para la cualificación de las estructuras de gobernanza del espacio. Abstract: A essay intending to analyze, from the philosophical dissertation, the events that take place around the application of the risk allocation policy and population resettlement in Bogotá, to verify their quality as a manifestation of confrontations between the hegemonic power exercised through the State and the roots as an expression of the living forces of social heterogeneity, highlighting such heteogeneity as fundamental information for the qualification of the governance structures of the space

    Understanding The Genre Fluid Movement: Do Genres Matter Any More in the Recording Industry? = Entendiendo la fluidez de los géneros musicales: ¿Los géneros musicales siguen siendo importantes en la industria fonográfica?

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    Abstract: The purpose of this research is to investigate if traditional genre structures created by the music industry are still relevant in today’s world. In the last few years, we have seen a high influx of artists blending influences and sounds - this phenomenon is what we call genre fluidity. This study will tackle pinnacle concepts, defining a genre, the evolution of popular music genres, and finally the relationship of genres with the Recording Industry and some of its stakeholders. In order to understand this phenomenon, data analysis of music charts and interviews with record label stakeholders took place. Resumen: Esta investigación indaga cuestiona si las estructuras de género tradicionales creadas por la industria de la música siguen siendo relevantes en el mundo actual. En los últimos años hemos visto un gran número de artistas que mezclan influencias y sonidos; este fenómeno es lo que llamamos fluidez de género. Este estudio abordará conceptos cumbre, la definición de un género, la evolución de los géneros musicales populares y, finalmente, la relación de los géneros con la industria discográfica y algunos de sus grupos de interés. Para entender este fenómeno, se llevó a cabo el análisis de datos de los Billboard Charts y entrevistas con las partes interesadas de los sellos discográficos
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