9 research outputs found

    Efficient Bayesian estimation of the generalized Langevin equation from data

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    The generalized Langevin equation (GLE) overcomes the limiting Markov approximation of the Langevin equation by an incorporated memory kernel and can be used to model various stochastic processes in many fields of science ranging from climate modeling over neuroscience to finance. Generally, Bayesian estimation facilitates the determination of both suitable model parameters and their credibility for a measured time series in a straightforward way. In this work we develop a realization of this estimation technique for the GLE in the case of white noise. We assume piecewise constant drift and diffusion functions and represent the characteristics of the data set by only a few coefficients, which leads to a numerically efficient procedure. The kernel function is an arbitrary time-discrete function with a fixed length KK. We show how to determine a reasonable value of KK based on the data. We illustrate the abilities of both the method and the model by an example from turbulence

    Non-parametric estimation of a Langevin model driven by correlated noise

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    Langevin models are frequently used to model various stochastic processes in different fields of natural and social sciences. They are adapted to measured data by estimation techniques such as maximum likelihood estimation, Markov chain Monte Carlo methods, or the non-parametric direct estimation method introduced by Friedrich et al. The latter has the distinction of being very effective in the context of large data sets. Due to their δ\delta-correlated noise, standard Langevin models are limited to Markovian dynamics. A non-Markovian Langevin model can be formulated by introducing a hidden component that realizes correlated noise. For the estimation of such a partially observed diffusion a different version of the direct estimation method was introduced by Lehle et al. However, this procedure includes the limitation that the correlation length of the noise component is small compared to that of the measured component. In this work we propose another version of the direct estimation method that does not include this restriction. Via this method it is possible to deal with large data sets of a wider range of examples in an effective way. We discuss the abilities of the proposed procedure using several synthetic examples

    Adaptive stochastic continuation with a modified lifting procedure applied to complex systems

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    Many complex systems occurring in the natural or social sciences or economics are frequently described on a microscopic level, e.g., by lattice- or agent-based models. To analyse the solution and bifurcation structure of such systems on the level of macroscopic observables one has to rely on equation-free methods like stochastic continuation. Here, we investigate how to improve stochastic continuation techniques by adaptively choosing the model parameters. This allows one to obtain bifurcation diagrams quite accurately, especially near bifurcation points. We introduce lifting techniques which generate microscopic states with a naturally grown structure, which can be crucial for a reliable evaluation of macroscopic quantities. We show how to calculate fixed points of fluctuating functions by employing suitable linear fits. This procedure offers a simple measure of the statistical error. We demonstrate these improvements by applying the approach to give an analysis of (i) the Ising model in two dimensions, (ii) an active Ising model and (iii) a stochastic Swift-Hohenberg equation. We conclude by discussing the abilities and remaining problems of the technique

    A review of nitrous oxide mitigation by farm nitrogen management in temperate grassland-based agriculture

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    peer-reviewedNitrous oxide (N2O) emission from grassland-based agriculture is an important source of atmospheric N2O. It is hence crucial to explore various solutions including farm nitrogen (N) management to mitigate N2O emissions without sacrificing farm profitability and food supply. This paper reviews major N management practices to lower N2O emission from grassland-based agriculture. Restricted grazing by reducing grazing time is an effective way to decrease N2O emissions from excreta patches. Balancing the protein-to-energy ratios in the diets of ruminants can also decrease N2O emissions from excreta patches. Among the managements of synthetic fertilizer N application, only adjusting fertilizer N rate and slow-released fertilizers are proven to be effective in lowering N2O emissions. Use of bedding materials may increase N2O emissions from animal houses. Manure storage as slurry, manipulating slurry pH to values lower than 6 and storage as solid manure under anaerobic conditions help to reduce N2O emissions during manure storage stage. For manure land application, N2O emissions can be mitigated by reducing manure N inputs to levels that satisfy grass needs. Use of nitrification inhibitors can substantially lower N2O emissions associated with applications of fertilizers and manures and from urine patches. N2O emissions from legume based grasslands are generally lower than fertilizer-based systems. In conclusion, effective measures should be taken at each step during N flow or combined options should be used in order to mitigate N2O emission at the farm level.Department of Agriculture, Fisheries and Food, Ireland, Research Stimulus Fund (RSF 07 516, RSF 07 511

    Comparison of the Quantra QPlus and ROTEM Goal-Directed Transfusion Protocols in Cardiothoracic Surgery Patients:A Prospective Observational Study

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    OBJECTIVES: To compare the designed treatment protocols for the Quantra QPlus and rotational thromboelastometry (ROTEM) with regard to transfusion advice. DESIGN: Prospective observational study. SETTING: Maastricht University Medical Center, The Netherlands. PARTICIPANTS: Adults with elective cardiopulmonary bypass surgery with a ROTEM test. INTERVENTIONS: ROTEM tests were performed postoperatively for standard monitoring of coagulation status and clinical decision making. Simultaneously, a concurrent sample was analyzed for the Quantra QPlus. MEASUREMENTS AND MAIN RESULTS: A total of 100 samples were analyzed using both the ROTEM and Quantra QPlus. Agreement between the transfusion advice for the ROTEM and Quantra QPlus protocols were compared using Cohen ? values for i.a. fibrinogen, platelet concentrates, and fresh frozen plasma (FFP). The agreement between ROTEM and Quantra QPlus was poor for overall transfusion (0.174) and fibrinogen transfusion (0.300). The agreement of cutoff values for fibrinogen clot stiffness for the Quantra QPlus and EXTEM A10 for the ROTEM was poor (0.160). The fibrinogen clot stiffness and FIBTEM A10 had a moderate agreement (0.731). A Cohen ? could not be calculated for the agreement of protamine, thrombocytes, FFP or cutoff values for these transfusions since frequencies included zero in these cases. The Quantra QPlus transfusion protocol advises transfusion in many non-bleeders, adjustments appear to be necessary. In a small group of cases in which clinically relevant blood loss was observed, the Quantra QPlus advised administration of transfusion products, whereas the ROTEM tests did not. CONCLUSION: ROTEM-guided and Quantra-guided transfusion did not correspond in this patient group, and agreement was moderate at best. Specificity and sensitivity for transfusion within protocols were heterogeneous between the methods. More clinical research in high-bleeding risk populations is needed to determine the clinical impact of the different protocols

    New approaches for the development and application of monoclonal antibodies for the diagnosis and therapy of human cancer

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