Algunos aspectos practicos en los contratos privados de construccion.

73 p.La realidad de la práctica de los contratos de obras de ingeniería es un campo bastante desconocido dentro del ámbito jurídico, básicamente por su especificidad técnica. El objetivo de la presente memoria es analizar algunos aspectos prácticos de los contratos de obras de ingeniería, así como la normativa aplicable tanto en el Código Civil, Código de Comercio y otras normativas de la especialidad como es la Ley General de Urbanismo y Construcción. El enfoque está dirigido a la prevención, detección y resolución de conflictos de carácter contractual, normalmente a través de la jurisdicción arbitral, analizando entre otras materias la teoría de la imprevisión la cual es aplicable en cierto tipo de contratos de obras. Por ser el tema en general bastante extenso, solo se han podido abordar los principales aspectos prácticos que se presentan en éstos contratos desde mi experiencia personal como Ingeniero Civil

Methods versus substance: measuring the effects of technology shocks on hours

In this paper, we employ both calibration and modern (Bayesian) estimation methods to assess the role of neutral and investment-specific technology shocks in generating fluctuations in hours. Using a neoclassical stochastic growth model, we show how answers are shaped by the identification strategies and not by the statistical approaches. The crucial parameter is the labor supply elasticity. Both a calibration procedure that uses modern assessments of the Frisch elasticity and the estimation procedures result in technology shocks accounting for 2% to 9% of the variation in hours worked in the data. We infer that we should be talking more about identification and less about the choice of particular quantitative approaches.Business cycles ; Technology - Economic aspects

Implementation of a Large-Scale Platform for Cyber-Physical System Real-Time Monitoring

The emergence of Industry 4.0 and the Internet of Things (IoT) has meant that the manufacturing industry has evolved from embedded systems to cyber-physical systems (CPSs). This transformation has provided manufacturers with the ability to measure the performance of industrial equipment by means of data gathered from on-board sensors. This allows the status of industrial systems to be monitored and can detect anomalies. However, the increased amount of measured data has prompted many companies to investigate innovative ways to manage these volumes of data. In recent years, cloud computing and big data technologies have emerged among the scientific communities as key enabling technologies to address the current needs of CPSs. This paper presents a large-scale platform for CPS real-time monitoring based on big data technologies, which aims to perform real-time analysis that targets the monitoring of industrial machines in a real work environment. This paper is validated by implementing the proposed solution on a real industrial use case that includes several industrial press machines. The formal experiments in a real scenario are conducted to demonstrate the effectiveness of this solution and also its adequacy and scalability for future demand requirements. As a result of the implantation of this solution, the overall equipment effectiveness has been improved.The authors are grateful to Goizper and Fagor Arrasate for providing the industrial case study, and specifically Jon Rodriguez and David Chico (Fagor Arrasate) for their help and support. Any opinions, findings and conclusions expressed in this article are those of the authors and do not necessarily reflect the views of the funding agencies

Deeper Networks for Pavement Crack Detection

Pavement crack detection using computer vision techniques has been studied widely over the past several years. However, these techniques have faced several limitations when applied to real world situations due to for example changes of lightning conditions or variation in textures. But the recent advancements in the field of artificial neural networks, especially in deep learning, have paved a new way for applying computer vision methods to pavement crack detection. In this paper we demonstrate the effectiveness of deep networks in computer vision based pavement crack detection. We also show how variations in location of training and testing datasets affect the performance of the deep learning based pavement crack detection method