Assessing agglomeration economies in the Yangzi River Delta, China : a bayesian spatial econometric approach

This paper estimates the elasticity of labor productivity with respect to employment density, a widely used measure of the agglomeration effect, in the Yangtze River Delta, China. A spatial Durbin model is presented that makes explicit the influences of spatial dependence and endogeneity bias in a very simple way. Results of Bayesian estimation using the data of the year 2009 indicate that the productivity is influenced by factors correlated with density rather than density itself and that spatial spillovers of these factors of agglomeration play a significant role. They are consistent with the findings of Ke (2010) and Artis, et al. (2011) that suggest the importance of taking into account spatial dependence and hitherto omitted variables.

Agglomeration and firm-level productivity : a Bayesian spatial approach

This paper estimates the impact of industrial agglomeration on firm-level productivity in Chinese manufacturing sectors. To account for spatial autocorrelation across regions, we formulate a hierarchical spatial model at the firm level and develop a Bayesian estimation algorithm. A Bayesian instrumental-variables approach is used to address endogeneity bias of agglomeration. Robust to these potential biases, we find that agglomeration of the same industry (i.e. localization) has a productivity-boosting effect, but agglomeration of urban population (i.e. urbanization) has no such effects. Additionally, the localization effects increase with educational levels of employees and the share of intermediate inputs in gross output. These results may suggest that agglomeration externalities occur through knowledge spillovers and input sharing among firms producing similar manufactures.

Resistencia a compresión de bloques de tierra comprimidad estabilizada con materiales de sílice de diferente tamaño de partícula.

Los bloques de tierra comprimida (BTC) están actualmente siendo estudiados en gran parte del mundo con diferentes estabilizantes para mejorar diversas de sus características. Esta situación es debida a la importancia que la tierra cruda tiene en el planeta como material de construcción. Su fácil disponibilidad, bajo coste e inercia térmica hacen de la tierra una materia prima fundamental para las viviendas de ciertas poblaciones en el mundo. El objetivo fundamental del presente Trabajo Fin de Máster es determinar si existe una relación del tamaño de partícula del material que aporta la sílice en relación a la reacción química con la cal hidratada para el aumento de la resistencia a compresión de los BTC. Para la diferenciación de los tamaños de partícula de sílice en este estudio se ha utilizado arcilla como componente control de las probetas ensayadas y una pequeña cantidad de nanosílice para comparar con las probetas sin este nanomaterial y determinar así la influencia del tamaño de partícula. Así mismo se han ensayado otros tipos de probetas para confirmar la influencia de la compactación mecánica en los BTC y de la cantidad de agua para considerar un equilibrio entre la resistencia y la trabajabilidad de los BTC. Durante la redacción del trabajo se repasará la importancia comentada de la tierra cruda en la construcción mundial y las principales técnicas que la han usado a lo largo de la historia para sentar las bases de los BTC actuales. También se comentarán y valorarán en lo relativo a los estabilizantes y la resistencia a compresión una serie de investigaciones publicadas con aspectos semejantes a los tratados en este estudio. A continuación se procederá a una explicación de los métodos ejercidos para los ensayos de las probetas de BTC correspondientes a este trabajo para más tarde realizar una discusión de los resultados obtenidos. Con todo ello se establecerán unas conclusiones en lo referente a los objetivos del trabajo.

Road environment modeling using robust perspective analysis and recursive Bayesian segmentation

Recently, vision-based advanced driver-assistance systems (ADAS) have received a new increased interest to enhance driving safety. In particular, due to its high performance–cost ratio, mono-camera systems are arising as the main focus of this field of work. In this paper we present a novel on-board road modeling and vehicle detection system, which is a part of the result of the European I-WAY project. The system relies on a robust estimation of the perspective of the scene, which adapts to the dynamics of the vehicle and generates a stabilized rectified image of the road plane. This rectified plane is used by a recursive Bayesian classi- fier, which classifies pixels as belonging to different classes corresponding to the elements of interest of the scenario. This stage works as an intermediate layer that isolates subsequent modules since it absorbs the inherent variability of the scene. The system has been tested on-road, in different scenarios, including varied illumination and adverse weather conditions, and the results have been proved to be remarkable even for such complex scenarios.

Learning an L1-regularized Gaussian Bayesian Network in the Equivalence Class Space

Learning the structure of a graphical model from data is a common task in a wide range of practical applications. In this paper, we focus on Gaussian Bayesian networks, i.e., on continuous data and directed acyclic graphs with a joint probability density of all variables given by a Gaussian. We propose to work in an equivalence class search space, specifically using the k-greedy equivalence search algorithm. This, combined with regularization techniques to guide the structure search, can learn sparse networks close to the one that generated the data. We provide results on some synthetic networks and on modeling the gene network of the two biological pathways regulating the biosynthesis of isoprenoids for the Arabidopsis thaliana plant