2,126 research outputs found

    User reflection on actions in ambulance telemedicine systems

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    Which news moves the euro area bond market?

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    This paper explores a long dataset (1999-2005) of intraday prices on German long-term bond futures and examines market responses to major macroeconomic announcements and ECB monetary policy releases. In general, adjustments in prices are quick and new information is usually incorporated into prices within five minutes of announcements. The volatility adjustment is more long-lasting than that in the conditional mean, and excess volatility can be observed up to 30 minutes after the releases. Overall, German bond markets tend to react more strongly to the surprise component in US macro releases compared to euro area and domestic releases, and the strength of those reactions to US releases has increased over the period considered. The paper also provides evidence that the outcome of German unemployment figures has been known to investors ahead of the prescheduled release. JEL Classification: E43, E44, E58intraday data, macroeconomic announcements, monetary policy

    Stable and efficient time integration of a dynamic pore network model for two-phase flow in porous media

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    We study three different time integration methods for a dynamic pore network model for immiscible two-phase flow in porous media. Considered are two explicit methods, the forward Euler and midpoint methods, and a new semi-implicit method developed herein. The explicit methods are known to suffer from numerical instabilities at low capillary numbers. A new time-step criterion is suggested in order to stabilize them. Numerical experiments, including a Haines jump case, are performed and these demonstrate that stabilization is achieved. Further, the results from the Haines jump case are consistent with experimental observations. A performance analysis reveals that the semi-implicit method is able to perform stable simulations with much less computational effort than the explicit methods at low capillary numbers. The relative benefit of using the semi-implicit method increases with decreasing capillary number Ca\mathrm{Ca}, and at Ca∌10−8\mathrm{Ca} \sim 10^{-8} the computational time needed is reduced by three orders of magnitude. This increased efficiency enables simulations in the low-capillary number regime that are unfeasible with explicit methods and the range of capillary numbers for which the pore network model is a tractable modeling alternative is thus greatly extended by the semi-implicit method.Comment: 33 pages, 12 figure

    Predicting the impact of academic articles on marketing research: Using machine learning to predict highly cited marketing articles

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    The citation count of an academic article is of great importance to researchers and readers. Due to the large increase in the publication of academic articles every year, it may be difficult to recognize the articles which are important to the field. This thesis collected data from Scopus with the purpose to analyze how paper, journal, and author related variables performed as drivers of article impact in the marketing field, and how well they could predict highly cited articles five years ahead in time. Social network analysis was used to find centrality metrics, and citation count one year after publication was included as the only time dependent variable. Our results found that citations after one year is a strong driver and predictor for future citations after five years. The analysis of the co-authorship network showed that closeness centrality and betweenness centrality are drivers of future citations in the marketing field, indicating that being close to the core of the network and having brokerage power is important in the field. With the use of machine learning methods, we found that a combination of paper, journal, and author related drivers perform better at predicting highly cited articles after five years, compared to using only one type of driver.nhhma