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A common way for a distributed system to tolerate crashes is to explicitly detect them and then recover from them. Interestingly, detection can take much longer than recovery, as a result of many advances in recovery techniques, making failure detection the dominant factor in these systems' unavailability when a crash occurs.This paper presents the design, implementation, and evaluation of Falcon, a failure detector with several features. First, Falcon's common-case detection time is sub-second, which keeps unavailability low. Second, Falcon is reliable: it never reports a process as down when it is actually up. Third, Falcon sometimes kills to achieve reliable detection but aims to kill the smallest needed component. Falcon achieves these features by coordinating a network of spies, each monitoring a layer of the system. Falcon's main cost is a small amount of platform-specific logic. Falcon is thus the first failure detector that is fast, reliable, and viable. As such, it could change the way that a class of distributed systems is built.

Ultra‐wide bandgap semiconductor samarium oxide attracts great interest because of its high stability and electronic properties. However, the ionic transport properties of Sm2O3 have rarely been studied. In this work, Ni doping is proposed to be used for electronic structure engineering of Sm2O3. The formation of Ni‐doping defects lowers the Fermi level to induce a local electric field, which greatly enhances the proton transport at the surface. Furthermore, ascribed to surface modification, the high concentration of vacancies and lattice disorder on the surface layer promote proton transport. A high‐performance of 1438 mW cm–2 and ionic conductivity of 0.34 S cm–1 at 550 °C have been achieved using 3% mol Ni doped Sm2O3 as electrolyte for fuel cells. The well‐dispersed Ni doped surface in Sm2O3 builds up continuous surfaces as proton channels for high‐speed transport. In this work, a new methodology is presented to develop high‐performance, low‐temperature ceramic fuel cells.

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in AbstractWe present estimates of the term structure of inflation expectations, derived from an affine model of real and nominal yield curves. The model features stochastic covariation of inflation with the real pricing kernel, enabling us to extract a time-varying inflation risk premium. We fit the model not only to yields, but also to the yields' variancecovariance matrix, thus increasing identification power. We find that model-implied inflation expectations can differ substantially from break-even inflation rates when market volatility is high. Our model's ability to be updated weekly makes it suitable for real-time monetary policy analysis.

This paper investigates the forecasting ability of four different GARCH models and the Kalman filter method. The four GARCH models applied are the bivariate GARCH, BEKK GARCH, GARCH-GJR and the GARCH-X model. The paper also compares the forecasting ability of the non-GARCH model: the Kalman method. Forecast errors based on 20 UK company daily stock return (based on estimated time-varying beta) forecasts are employed to evaluate out-of-sample forecasting ability of both GARCH models and Kalman method. Measures of forecast errors overwhelmingly support the Kalman filter approach. Among the GARCH models the GJR model appears to provide somewhat more accurate forecasts than the other bivariate GARCH models. Copyright © 2008 John Wiley & Sons, Ltd.

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Bulk high-temperature superconductors (HTSs) have a high critical current density and can trap a large magnetic field. When bulk superconductors are magnetized by the pulsed field magnetization (PFM) technique, they are also subjected to a large electromagnetic stress, and the resulting thermal stress may cause cracking of the superconductor due to the brittle nature of the sample. In this paper, based on the H-formulation and the law of heat transfer, we can obtain the distributions of electromagnetic field and temperature, which are in qualitative agreement with experiment. After that, based on the dynamic equilibrium equations, the mechanical response of the bulk superconductor is determined. During the PFM process, the change in temperature has a dramatic effect on the radial and hoop stresses, and the maximum radial and hoop stress are 24.2 MPa and 22.6 MPa, respectively. The mechanical responses of a superconductor for different cases are also studied, such as the peak value of the applied field and the size of bulk superconductors. Finally, the stresses are also presented for different magnetization methods.