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An effective procedure has been developed to consolidate and hydrophobize decayed monumental stones by a simple sol-gel process. The sol contains silica oligomer, silica nanoparticles and a surfactant, preventing gel cracking. The effectiveness of the process on biocalcareous stone samples from an 18th century cathedral has been evaluated, and it was found that the gel creates effective linking bridges between mineral grains of the stone. Silica nanoparticles produced a significant increase in the mechanical resistance and cohesion of the stone. The application of an additional fluorinated oligomer onto the consolidated stone gave rise to a surface with lasting hydrophobicity, preventing water absorption.

Spatially-referenced geostatistical responses that are collected in environmental sciences research are often subject to detection limits, where the measures are not fully quantifiable. This leads to censoring (left, right, interval, etc), and various ad hoc statistical methods (such as choosing arbitrary detection limits, or data augmentation) are routinely employed during subsequent statistical analysis for inference and prediction. However, inference may be imprecise and sensitive to the assumptions and approximations involved in those arbitrary choices. To circumvent this, we propose an maximum likelihood estimation framework of the fixed effects and variance components and related prediction via a novel application of the Stochastic Approximation of the Expectation Maximization (SAEM) algorithm, allowing for easy and elegant estimation of model parameters under censoring. Both simulation studies and application to a real dataset on arsenic concentration collected by the Michigan Department of Environmental Quality demonstrate the advantages of our method over the available naïve techniques in terms of finite sample properties of the estimates, prediction, and robustness. The proposed methods can be implemented using the R package CensSpatial.

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El propósito de este trabajo es explorar, a partir de los datos de la encuesta GEM 2012, los factores que pueden afectar la probabilidad de que un colombiano que ha retornado al país, sea emprendedor por oportunidad. La exploración se hizo a partir de la estimación de modelos de respuesta binaria. Los resultados muestran que las variables que mejor explican que un migrante retornado emprenda por oportunidad son: las expectativas positivas en el país para la creación de empresas en un horizonte de seis meses, los ahorros, y los contactos realizados durante su permanencia en el exterior. Este trabajo es uno de los primeros en abordar los factores que afectan la probabilidad de que los migrantes retornados sean emprendedores por oportunidad, sin embargo, no deja de ser de carácter exploratorio, debido a las limitaciones en las fuentes de información disponibles. Se requieren más investigaciones que profundicen en el tema.