Disasters in the media: a content analysis of the March 2011 Japan earthquake/tsunami and nuclear disasters

2012 Fall.Includes bibliographical references.This cross-cultural study analyzed online newspaper stories about the March 2011 Japan earthquake/tsunami and nuclear disasters from two nationally representative newspapers: the NYTimes.com in the United States and the Yomiuri Shimbun in Japan. This study investigated stories published between March 11, 2011, and April 15, 2011. These online news stories were examined to determine their use of disaster myths, past disasters, media hype, and directly quoted sources. Results show that few disaster myths were used overall; however, there was a difference in the number of panic flight myths used in the newspapers studied. References to past disasters were also measured on whether or not they were in the story. It was more common that stories did not mention a past disaster when reporting about the March 2011 Japan earthquake/tsunami and nuclear disasters. The NYTimes.com used more past disasters in its stories than did the Yomiuri Shimbun; Chernobyl was the most frequently mentioned past disaster. Results also show that there was no evidence of a nuclear crisis media hype in the newspapers during the time period analyzed. The study also investigated differences in the use of sources by the two newspapers. Counter to predictions, the Yomiuri Shimbun did not use more official sources than the NYTimes.com. The results suggest that culture played a role in the two newspapers covered the disaster. A closer investigation into each of the variables investigated in this study and the role of culture in reporting about disasters may be warranted in future research

Laser clad and HVOF sprayed Stellite 6 coating in chlorine rich environment with KCI at 700 °C

Laser clads and HVOF coatings from a stellite 6 alloy (Co–Cr–W–C alloy) on 304 stainless steel substrates were exposed both bare and with KCl deposits in 500 ppm HCl with 5% O2 for 250 h at 700 C. SEM/EDX and PXRD analyses with Rietveld refinement were used for assessment of the attack and for analysis of the scales. The bare samples suffered from scale spallation and the scale was mostly composed of Cr2O3, CoCr2O4 and CoO, although due to dilution haematite (Fe2O3) was detected in the scale formed on the laser clad sample. A small amount of hydrated HCl was detected in bare samples. While the corrosion of the bare surfaces was limited to comparatively shallow depths and manifested by g and M7C3 carbide formation, the presence of KCl on the surface led to severe Cr depletion from the HVOF coating (to 1 wt%). Both inward and outward diffusion of elements occurred in the HVOF coating resulting in Kirdendall voids at the coating–steel interface. The laser clad sample performed significantly better in conditions of the KCl deposit-induced corrosion. In addition to the oxides, CoCl2 was detected in the HVOF sample and K3CrO4 was detected in the laser clad sample. Thermodynamic calculations and kinetic simulations were carried out to interpret the oxidation and diffusion behaviours of coatings

The Lick AGN Monitoring Project 2016: Dynamical Modeling of Velocity-Resolved H\b{eta} Lags in Luminous Seyfert Galaxies

We have modeled the velocity-resolved reverberation response of the H\b{eta} broad emission line in nine Seyfert 1 galaxies from the Lick Active Galactic Nucleus (AGN) Monitioring Project 2016 sample, drawing inferences on the geometry and structure of the low-ionization broad-line region (BLR) and the mass of the central supermassive black hole. Overall, we find that the H\b{eta} BLR is generally a thick disk viewed at low to moderate inclination angles. We combine our sample with prior studies and investigate line-profile shape dependence, such as log10(FWHM/{\sigma}), on BLR structure and kinematics and search for any BLR luminosity-dependent trends. We find marginal evidence for an anticorrelation between the profile shape of the broad H\b{eta} emission line and the Eddington ratio, when using the root-mean-square spectrum. However, we do not find any luminosity-dependent trends, and conclude that AGNs have diverse BLR structure and kinematics, consistent with the hypothesis of transient AGN/BLR conditions rather than systematic trends

The Lick AGN Monitoring Project 2016 : dynamical modeling of velocity-resolved Hβ lags in luminous Seyfert galaxies

K.H. acknowledges support from STFC grant ST/R000824/1.We have modeled the velocity-resolved reverberation response of the Hβ broad emission line in nine Seyfert 1 galaxies from the Lick Active Galactic Nucleus (AGN) Monitoring Project 2016 sample, drawing inferences on the geometry and structure of the low-ionization broad-line region (BLR) and the mass of the central supermassive black hole. Overall, we find that the Hβ BLR is generally a thick disk viewed at low to moderate inclination angles. We combine our sample with prior studies and investigate line-profile shape dependence, such as log10(FWHM/σ), on BLR structure and kinematics and search for any BLR luminosity-dependent trends. We find marginal evidence for an anticorrelation between the profile shape of the broad Hβ emission line and the Eddington ratio, when using the rms spectrum. However, we do not find any luminosity-dependent trends, and conclude that AGNs have diverse BLR structure and kinematics, consistent with the hypothesis of transient AGN/BLR conditions rather than systematic trends.Publisher PDFPeer reviewe

The Lick AGN Monitoring Project 2016 : velocity-resolved Hβ lags in luminous Seyfert galaxies

Funding: K.H. acknowledges support from STFC grant ST/R000824/1.We carried out spectroscopic monitoring of 21 low-redshift Seyfert 1 galaxies using the Kast double spectrograph on the 3 m Shane telescope at Lick Observatory from April 2016 to May 2017. Targetingactive galactic nuclei (AGN) with luminosities of λLλ(5100 Å) ≈ 1044 erg s−1 and predicted Hβ lags of∼ 20–30 days or black hole masses of 107–108.5 M⊙, our campaign probes luminosity-dependent trends in broad-line region (BLR) structure and dynamics as well as to improve calibrations for single-epoch estimates of quasar black hole masses. Here we present the first results from the campaign, including Hβ emission-line light curves, integrated Hβ lag times (8–30 days) measured against V -band continuum light curves, velocity-resolved reverberation lags, line widths of the broad Hβ components, and virial black hole mass estimates (107.1–108.1 M⊙). Our results add significantly to the number of existing velocity-resolved lag measurements and reveal a diversity of BLR gas kinematics at moderately high AGN luminosities. AGN continuum luminosity appears not to be correlated with the type of kinematics that its BLR gas may exhibit. Follow-up direct modeling of this dataset will elucidate the detailed kinematics and provide robust dynamical black hole masses for several objects in this sample.Publisher PDFPeer reviewe

AQ-Bench: a benchmark dataset for machine learning on global air quality metrics

With the AQ-Bench dataset, we contribute to the recent developments towards shared data usage and machine learning methods in the field of environmental science. The dataset presented here enables researchers to relate global air quality metrics to easy-access metadata and to explore different machine learning methods for obtaining estimates of air quality based on this metadata. AQ-Bench contains a unique collection of aggregated air quality data from the years 2010–2014 and metadata at more than 5500 air quality monitoring stations all over the world, provided by the first Tropospheric Ozone Assessment Report (TOAR). It focuses in particular on metrics of tropospheric ozone, which has a detrimental effect on climate, human morbidity and mortality, as well as crop yields. The purpose of this dataset is to produce estimates of various long-term ozone metrics based on time-independent local site conditions. We combine this task with a suitable evaluation metric. Baseline scores obtained from a linear regression method, a fully connected neural network and random forest are provided for reference and validation. AQ-Bench offers a low-threshold entrance for all machine learners with an interest in environmental science and for atmospheric scientists who are interested in applying machine learning techniques. It enables them to start with a real-world problem relevant to humans and nature. The dataset and introductory machine learning code are available at https://doi.org/10.23728/b2share.30d42b5a87344e82855a486bf2123e9f (Betancourt et al., 2020) and https://gitlab.version.fz-juelich.de/esde/machine-learning/aq-bench (Betancourt et al., 2021). AQ-Bench thus provides a blueprint for environmental benchmark datasets as well as an example for data re-use according to the FAIR principles