Improvement of Longitudinal Joints in Asphalt Pavement, HR-215, Construction Report, 1981

One significant benefit of asphalt concrete pavement construction is that it may be opened to traffic within one hour after being laid. Therefore, road closure and detour are not necessary, but only temporary lane closure and control of traffic. This one lane construction, even though desirable in regard to maintaining traffic flow, does pose an additional problem. The longitudinal joint at centerline often becomes a maintenance problem. The objective of this research project is to identify construction procedures that will provide an improved centerline joint.

Los valores éticos en la responsabilidad social corporativa

La significación de responsabilidad social corporativa (RSC) o empresa socialmente responsable (RSE) ha pasado de ser un concepto abstracto y mal entendido, a ser un apellido que otorga prestigio a las empresas que deciden adoptarlo. Las empresas han adoptado y adaptado esta concepción con el objetivo de seguir lucrándose con sus actividades empresariales, pero poseyendo un argumento perfecto para venderse ante una sociedad cada vez mejor educada en temas sociales y medioambientales: ser socialmente responsable o, lo que es lo mismo, buena con el entorno más próximo que le rodea y en el que crece.Se puede afirmar que una empresa es socialmente responsable cuando su modelo de actuación promueve el desarrollo de prácticas transparentes en el foro interno y externo de la empresa, marco de actuación que no solo se limita al área de confort de la organización sino a un saber hacer ético. Los códigos éticos y corporativos deben ser accesibles y comunicados correctamente ante todos los públicos de una empresa. El objetivo de este estudio se centra en analizar la relación existente entre la Ética y la RSC, ya que ambas deben ser inseparables en la construcción del eje transversal que recorre la estrategia organizacional. Se desarrolla una metodología de tipo cualitativo, justificado por el enfoque conceptual-teórico desde el punto de vista social, cultural y económico del tema objeto de estudio. 

Development of unsupervised learning methods with applications to life sciences data

Machine Learning makes computers capable of performing tasks typically requiring human intelligence. A domain where it is having a considerable impact is the life sciences, allowing to devise new biological analysis protocols, develop patients’ treatments efficiently and faster, and reduce healthcare costs. This Thesis work presents new Machine Learning methods and pipelines for the life sciences focusing on the unsupervised field. At a methodological level, two methods are presented. The first is an “Ab Initio Local Principal Path” and it is a revised and improved version of a pre-existing algorithm in the manifold learning realm. The second contribution is an improvement over the Import Vector Domain Description (one-class learning) through the Kullback-Leibler divergence. It hybridizes kernel methods to Deep Learning obtaining a scalable solution, an improved probabilistic model, and state-of-the-art performances. Both methods are tested through several experiments, with a central focus on their relevance in life sciences. Results show that they improve the performances achieved by their previous versions. At the applicative level, two pipelines are presented. The first one is for the analysis of RNA-Seq datasets, both transcriptomic and single-cell data, and is aimed at identifying genes that may be involved in biological processes (e.g., the transition of tissues from normal to cancer). In this project, an R package is released on CRAN to make the pipeline accessible to the bioinformatic Community through high-level APIs. The second pipeline is in the drug discovery domain and is useful for identifying druggable pockets, namely regions of a protein with a high probability of accepting a small molecule (a drug). Both these pipelines achieve remarkable results. Lastly, a detour application is developed to identify the strengths/limitations of the “Principal Path” algorithm by analyzing Convolutional Neural Networks induced vector spaces. This application is conducted in the music and visual arts domains.