This study examines for the first time the relationship among the oil price shocks and the sovereign yield spreads in the EMU (which is collectively the largest oil-importer of the world), in a ...time-varying environment. In particular, we examine the time-varying correlation between oil price shocks and the 10-year sovereign yield spread of core and periphery countries in the EMU, by employing a scalar-BEKK framework. The main findings reveal that the correlations between sovereign yield spreads and oil price shocks are indeed time-varying and are influenced by specific economic and geopolitical events that took place during the study period. Furthermore, even though the correlation patterns are constantly low or zero prior to the Great Recession, a change is revealed in the post-2008 period, when correlations become moderate and more volatile. Finally, we do not observe noteworthy differences in the correlation behaviour between core and periphery countries to different oil price shocks. The findings of this study are particularly useful and provide valuable information to marketplace participants.
•We assess the time-varying relationship between the oil price shocks and the 10-year sovereign yield spreads.•We focus on the European Monetary Union, which is the largest oil-importer of the world.•We show that the relationship is influenced by specific economic and geopolitical events.•Correlations are heterogenous depending on the oil price shock.•We do not find noteworthy differences in the correlation behaviour among core and periphery EMU countries.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Deep learning (DL)-based defect detection provides various robust automated solutions for maintaining the daily operations of electric multiple units (EMUs). However, a sophisticated detection model ...typically requires extensive, high-quality data. Therefore, effective detection of certain low-frequency defects can be challenging because it is impractical to collect a sufficient amount of data. To address the lack of targeted training data, a novel synthetic dataset generation method for defect detection of EMUs is proposed based on generative adversarial networks (GANs) and a 3-D CRH380A model. By using synthetic data, defect detection accuracy is significantly improved, even when the scope of real data is limited. The synthetic dataset is employed in this article to train three DL detection models (YOLO v5, Mask R-CNN, and TOOD), and comprehensive experiments are conducted to verify the proposed DefectGAN. The experimental results illustrate that the mixed dataset (synthetic and real data) can significantly improve defect detection accuracy for EMUs with limited data. With a few dozen real images, only mixing the synthetic images generated by the proposed DefectGAN into training datasets can increase the <inline-formula> <tex-math notation="LaTeX">{F}1 </tex-math></inline-formula> score from 0 to 0.8. This work will significantly improve the accuracy of defect detection of existing trouble of moving EMU detection system (TEDS) during data scarcity, facilitate automated operation and maintenance of EMUs, and reduce overall operating costs.
Objective To comprehensively understand the influence of carbody component dimensions on carbody modal characteristics, a sensitivity analysis of EMU (electric multiple units) body modal is conducted ...to enhance the main vibration frequency of carbody. Method Based on sensitivity calculation results, eight univariate thickness enhancement schemes are developed for comparison analysis of the main carbody modal. Subsequently, by pairing up components with the greatest impact on major vibration modal, three enhancement schemes are formulated to investigate the effects of multivariate thickness vibrations on the main carbody vibration modal while minimizing mass increase. Result & Conclusion The thickness of side top plates, circular top plates, side wall panels, side wall beams/columns, roof bending beams, corrugated floors and chassis edge beams exhibits high sensitivity to the main carbody vibration modal. Increasing the thickness of these univariate and multivariate influencing factors results in a noticeable
•The EMU-SDMS introduces a new set of speech database management and analysis tools that have been designed to work entirely in R.•The EMU-SDMS introduces a web based annotation tool for hierarchical ...and non-hierarchical annotation structures that can also be used for collaborative labeling efforts.•The EMU-SDMS introduces a package to perform common speech signal processing routines (e.g. formant calculation) in R.•The EMU-SDMS provides powerful general purpose annotation structure modeling capabilities that surpass those of many other systems.•The EMU-SDMS provides efficient query mechanics for complex hierarchical annotation structures.
The amount and complexity of the often very specialized tools necessary for working with spoken language databases has continually evolved and grown over the years. The speech and spoken language research community is expected to be well versed in multiple software tools and have the ability to switch seamlessly between the various tools, sometimes even having to script ad-hoc solutions to solve interoperability issues. In this paper, we present a set of tools that strive to provide an all-in-one solution for generating, manipulating, querying, analyzing and managing speech databases. The tools presented here are centered around the R language and environment for statistical computing and graphics (R Core Team, 2016), which benefits users by significantly reducing the number of tools the researchers have to familiarize themselves with. This paper introduces the next iteration of the EMU system that, although based on the core concepts of the legacy system, is a newly designed and almost entirely rewritten set of modern spoken language database management tools.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
Objective To ensure the reliability of the stator insulation in EMU (electric multiple unit) traction motors during stator manufacturing or maintenance, it is necessary to conduct tests such as ...ground insulation withstand voltage, interphase withstand voltage, and coil-to-coil insulation withstand voltage to screen for defects of stator-to-ground insulation, interphase insulation, and coil-to-coil insulation. Therefore, an analysis of stator insulation faults in traction motors is required, and corresponding countermeasures are proposed. Method The fault characteristics and causes of ground insulation breakdown, interphase short circuit, and non-coinciding waveform attenuation in coil-to-coil insulation are introduced. Corresponding improvement measures and stator repair methods for these faults are analyzed. Result & Conclusion After repair, the stator of traction motor successfully passes the screening tests for ground insulation withstand voltage, interphase withstand voltage, and coil-to-coil insula
U.S. estimates of the natural rate of interest – the real short-term interest rate that would prevail absent transitory disturbances – have declined dramatically since the start of the global ...financial crisis. For example, estimates using the Laubach–Williams (2003) model indicate the natural rate in the United States fell to close to zero during the crisis and has remained there into 2016. Explanations for this decline include shifts in demographics, a slowdown in trend productivity growth, and global factors affecting real interest rates. This paper applies the Laubach–Williams methodology to the United States and three other advanced economies – Canada, the Euro Area, and the United Kingdom. We find that large declines in trend GDP growth and natural rates of interest have occurred over the past 25years in all four economies. These country-by-country estimates are found to display a substantial amount of comovement over time, suggesting an important role for global factors in shaping trend growth and natural rates of interest.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Whatever It Takes Acharya, Viral V.; Eisert, Tim; Eufinger, Christian ...
The Review of financial studies,
09/2019, Volume:
32, Issue:
9
Journal Article
Peer reviewed
Open access
Launched in Summer 2012, the European Central Bank’s (ECB) Outright Monetary Transactions (OMT) program indirectly recapitalized European banks through its positive impact on periphery sovereign ...bonds. However, the stability reestablished in the banking sector did not fully translate into economic growth. We document zombie lending by banks that remained weakly capitalized even post-OMT. In turn, firms receiving loans used these funds not to undertake real economic activity, such as employment and investment, but to build cash reserves. Creditworthy firms in industries with a high zombie firm prevalence significantly suffered from this credit misallocation, which further slowed the economic recovery.
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BFBNIB, INZLJ, NMLJ, NUK, PNG, SAZU, UL, UM, UPUK, ZRSKP
The emu (Dromaius novaehollandiae) is a new poultry. In this study, we investigated the haplotype composition of mitochondrial DNA among emu populations farmed in Japan. We sequenced the D-loop ...region in 109 individuals, and detected four substitution sites and three haplotypes (Hap-a, -b, and -c). Hap-a was the most frequently observed haplotype in the Japanese populations. Although Hap-c was a rare haplotype in not only Japanese but also Australian populations, it was detected with high frequency in the Japanese farmed population. The AMOVA indicated that 9% of total variance was “among population”. The FST value was 0.087 and genetic differentiation was significant (P<0.01). These results may contribute to conserving the genetic resources available for the Japanese emu industry.
Measuring euro area monetary policy Altavilla, Carlo; Brugnolini, Luca; Gürkaynak, Refet S. ...
Journal of monetary economics,
12/2019, Volume:
108
Journal Article
Peer reviewed
Open access
•We map ECB policy communication into yield curve configurations using factor analysis.•Target, forward guidance and QE factors are estimated to influence the short-, medium-, and long-term segments ...of the yield curve, respectively.•Stock market response to monetary surprises depended on whether the announcements are perceived to reveal information about the state of the economy.•We show how to implement our methodology for any policy-related news release.•As a by-product, we present the Euro Area Monetary Policy Event-Study Database (EA-MPD).
We map ECB policy communication into yield curve changes and study the information flow on policy dates. A byproduct is the publicly available Euro Area Monetary Policy Event-Study Database (EA-MPD), containing intraday asset price changes. We find that Policy Target, Forward Guidance and Quantitative Easing factors capture about all the variation in the yield curve, with different factors appearing in the windows covering the policy decision announcement and the press conference, and having time-varying variance shares. We study sovereign yields, exchange rates, stock prices, persistence of effects and response asymmetry. Our methodology can be implemented for any policy-related event.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
This study examines the role of gold as a hedge or safe-haven asset in different phases of the COVID-19 pandemic crisis, corresponding to the timing of fiscal and monetary stimuli to support the ...weakened economy. Using high-frequency data, the results show that gold served as a safe-haven asset for stock markets during Phase I (December 31, 2019–March 16, 2020) of the pandemic. However, gold lost its safe-haven role during Phase II (March 17−April 24, 2020). The optimal weights of gold in S&P 500, Euro Stoxx 50, Nikkei 225, and WTI crude oil portfolios significantly increased during Phase II, suggesting that investors expanded investment in gold as a ‘flight-to-safety asset’ during the crisis. Further, hedging costs increased significantly during Phase II. These findings provide insight for individual and institutional investors and guidance to policymakers, regulators, and media on how gold evolved as a hedge and safe-haven asset in different phases of the pandemic.
•We study the role of gold in the COVID–19 crisis.•Gold acts as safe haven asset during Phase I (31 Dec 2019–16 Mar 2020).•Gold loses the safe haven status during Phase II (17 Mar −24 April 2020).•Investors increase their investment in gold during Phase II as a ‘flight-to-safety asset’.•Hedging cost increases during Phase II.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
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