Nitrous oxide emissions from European agriculture - an analysis of variability and drivers of emissions from field experiments

Nitrous oxide emissions from a network of agricultural experiments in Europe were used to explore the relative importance of site and management controls of emissions. At each site, a selection of management interventions were compared within replicated experimental designs in plot-based experiments. Arable experiments were conducted at Beano in Italy, El Encin in Spain, Foulum in Denmark, Logarden in Sweden, Maulde in Belgium CE1, Paulinenaue in Germany, and Tulloch in the UK. Grassland experiments were conducted at Crichton, Nafferton and Peaknaze in the UK, Godollo in Hungary, Rzecin in Poland, Zarnekow in Germany and Theix in France. Nitrous oxide emissions were measured at each site over a period of at least two years using static chambers. Emissions varied widely between sites and as a result of manipulation treatments. Average site emissions (throughout the study period) varied between 0.04 and 21.21 kg N2O-N ha−1yr−1, with the largest fluxes and variability associated with the grassland sites. Total nitrogen addition was found to be the single most important deter- minant of emissions, accounting for 15 % of the variance (using linear regression) in the data from the arable sites (p<0.0001), and 77 % in the grassland sites. The annual emissions from arable sites were significantly greater than those that would be predicted by IPCC default emission fac- tors. Variability of N2O emissions within sites that occurred as a result of manipulation treatments was greater than that resulting from site-to-site and year-to-year variation, highlighting the importance of management interventions in contributing to greenhouse gas mitigation

Dynamic Soil-Structure Interaction in Bridge Abutments

Strong motion obtained in instrumental short-span bridges show the importance of the abutments in the dynamic response of the whole structure. Many models have been used in order to take into account the influence of pier foundations although no reliable ones have been used to analyse the abutment performance. In this work three-dimensional Boundary Element models in frequency domain have been proposed and dimensionless dynamic stiffness of standard bridge abutments have been obtained.

Anisotropic magnetoresistive study in bilayer NiFe-NiO for sensor applications

Related with the detection of weak magnetic fields, the anisotropic magnetoresistive (AMR) effect is widely utilized in sensor applications. Exchange coupling between an antiferromagnet (AF) and the ferromagnet (FM) has been known as a significant parameter in the field sensitivity of magnetoresistance because of pinning effects on magnetic domain in FM layer by the bias field in AF. In this work we have studied the thermal evolution of the magnetization reversal processes in nanocrystalline exchange biased Ni80Fe20/Ni-O bilayers with large training effects and we report the anisotropic magnetoresistance ratio arising from field orientation in the bilayer.

Building lean thinking in a telecom software development organization: strengths and challenges.

The potential shown by Lean in different domains has aroused interest in the software industry. However, it remains unclear how Lean can be effectively applied in a domain such as software development that is fundamentally different from manufacturing. This study explores how Lean principles are implemented in software development companies and the challenges that arise when applying Lean Software Development. For that, a case study was conducted at Ericsson R&D Finland, which successfully adopted Scrum in 2009 and subsequently started a comprehensible transition to Lean in 2010. Focus groups were conducted with company representatives to help devise a questionnaire supporting the creation of a Lean mindset in the company (Team Amplifier). Afterwards, the questionnaire was used in 16 teams based in Finland, Hungary and China to evaluate the status of the transformation. By using Lean thinking, Ericsson R&D Finland has made important improvements to the quality of its products, customer satisfaction and transparency within the organization. Moreover, build times have been reduced over ten times and the number of commits per day has increased roughly five times.The study makes two main contributions to research. First, the main factors that have enabled Ericsson R&D?s achievements are analysed. Elements such as ?network of product owners?, ?continuous integration?, ?work in progress limits? and ?communities of practice? have been identified as being of fundamental importance. Second, three categories of challenges in using Lean Software Development were identified: ?achieving flow?, ?transparency? and ?creating a learning culture?

An optimal yet fast pruning algorithm to reduce latency in multiview prediction structures

We propose a new algorithm for the design of prediction structures with low delay and limited penalty in the rate-distortion performance for multiview video coding schemes. This algorithm constitutes one of the elements of a framework for the analysis and optimization of delay in multiview coding schemes that is based in graph theory. The objective of the algorithm is to find the best combination of prediction dependencies to prune from a multiview prediction structure, given a number of cuts. Taking into account the properties of the graph-based analysis of the encoding delay, the algorithm is able to find the best prediction dependencies to eliminate from an original prediction structure, while limiting the number of cut combinations to evaluate. We show that this algorithm obtains optimum results in the reduction of the encoding latency with a lower computational complexity than exhaustive search alternatives.

Fast 2D to 3D conversion using a clustering-based hierarchical search in a machine learning framework

Automatic 2D-to-3D conversion is an important application for filling the gap between the increasing number of 3D displays and the still scant 3D content. However, existing approaches have an excessive computational cost that complicates its practical application. In this paper, a fast automatic 2D-to-3D conversion technique is proposed, which uses a machine learning framework to infer the 3D structure of a query color image from a training database with color and depth images. Assuming that photometrically similar images have analogous 3D structures, a depth map is estimated by searching the most similar color images in the database, and fusing the corresponding depth maps. Large databases are desirable to achieve better results, but the computational cost also increases. A clustering-based hierarchical search using compact SURF descriptors to characterize images is proposed to drastically reduce search times. A significant computational time improvement has been obtained regarding other state-of-the-art approaches, maintaining the quality results.