Parental Influence on Weight Biases in School-Age Children

Obesity rates have rapidly increased in America over the past few decades, and with this rise comes an increase in the negative psychosocial consequences experienced by victims of weight bias. Although a fair amount of research on weight bias (i.e., the negative attitudes or beliefs one holds toward overweight individuals) has been done in adults and adolescents, limited research has been done in young children. This study worked to fill gaps in the literature by investigating if children between the ages of five and nine would show weight biases, if the biases against individuals would vary by the ethnicity and gender of the target, and if children’s biases related to parents’ biases and health habits. To measure bias, children completed an explicit Anti-Fat Attitudes Questionnaire and a more implicit Figure Rating Scale examining biases toward individuals of varying gender and ethnicity. Parents also completed the Anti-Fat Attitudes Questionnaire and a health habits survey. Children displayed significant biases against overweight individuals, with more bias relating to the controllability of obesity. Children did not show different biases toward individuals of different genders and ethnicity, nor did their biases relate to parental views

Superconductivity in potassium-doped 2,2′'-bipyridine

Organic compounds are always promising candidates of superconductors with high transition temperatures. We examine this proposal by choosing 2,2$'$-bipyridine solely composed by C, H, and N atoms. The presence of Meissner effect with a transition temperature of 7.2 K in this material upon potassium doping is demonstrated by the $dc$ magnetic susceptibility measurements. The real part of the $ac$ susceptibility exhibits the same transition temperature as that in $dc$ magnetization, and a sharp peak appeared in the imaginary part indicates the formation of the weakly linked superconducting vortex current. The occurence of superconductivity is further supported by the resistance drop at the transition together with its suppression by the applied magnetic fields. The superconducting phase is identified to be K$_3$-2,2$'$-bipyridine from the analysis of Raman scattering spectra. This work not only opens an encouraging window for finding superconductivity after optoelectronics in 2,2$'$-bipyridine-based materials but also offers an example to realize superconductivity from conducting polymers and their derivatives.Comment: 6 pages, 3 figure

Ratio of active mine land per metric ton of coal produced over time.

<p>Ratio of active mine land per metric ton of coal produced over time.</p

Mapping the yearly extent of surface coal mining in Central Appalachia using Landsat and Google Earth Engine — Yearly Mining Areas (KML)

<div>These data accompany the 2018 manuscript published in <i>PLOS One</i> titled "Mapping the yearly extent of surface coal mining in Central Appalachia using Landsat and Google Earth Engine". In this manuscript, researchers used the Google Earth Engine platform and freely-accessible Landsat imagery to create a yearly dataset (1985 through 2015) of surface coal mining in the Appalachian region of the United States of America. </div><div><br></div><div>This specific dataset is a collection of KML files of the mining areas as determined by this study for each year from 1985 through 2015. Individual file names within the dataset indicate the specific year. These files show the mining “footprint” in Appalachia for that given year, indicating that mining was occurring in a given location during that year. These files do not, however, indicate the year at which mining began or ceased in any given location.</div

Mapping the yearly extent of surface coal mining in Central Appalachia using Landsat and Google Earth Engine — exportImageryAccuracyAssessment.js (Processing Script)

<div>These data accompany the 2018 manuscript published in <i>PLOS One</i> titled "Mapping the yearly extent of surface coal mining in Central Appalachia using Landsat and Google Earth Engine". In this manuscript, researchers used the Google Earth Engine platform and freely-accessible Landsat imagery to create a yearly dataset (1985 through 2015) of surface coal mining in the Appalachian region of the United States of America. </div><div><br></div><div>This script, written in JavaScript, makes heavy use of the Google Earth Engine API. As such, this script is intended to be run in Earth Engine’s online and freely-accessible JavaScript IDE. </div><div><br></div><div>In particular, this script exports Landsat and NAIP imagery for use in accuracy assessment.</div

Mapping the yearly extent of surface coal mining in Central Appalachia using Landsat and Google Earth Engine — First Mining Year (GeoTIFF)

<div>These data accompany the 2018 manuscript published in <i>PLOS One</i> titled "Mapping the yearly extent of surface coal mining in Central Appalachia using Landsat and Google Earth Engine". In this manuscript, researchers used the Google Earth Engine platform and freely-accessible Landsat imagery to create a yearly dataset (1985 through 2015) of surface coal mining in the Appalachian region of the United States of America.</div><div><br></div><div>This specific dataset is a GeoTIFF file depicting when an area was first mined, from the period 1985 through 2015. The raster values depict the year that mining was first detected by the paper's processing model. A year of "1984" indicates mining that likely started at some point prior to 1985. These pre-1985 mining data are derived from a prior study; see https://skytruth.org/wp/wp-content/uploads/2017/03/SkyTruth-MTR-methodology.pdf for more information. This dataset does not indicate for how long an area was a mine or when mining ceased in a given area.</div

Mapping the yearly extent of surface coal mining in Central Appalachia using Landsat and Google Earth Engine — Most Recent Mining Year (GeoTIFF)

<div>These data accompany the 2018 manuscript published in <i>PLOS One</i> titled "Mapping the yearly extent of surface coal mining in Central Appalachia using Landsat and Google Earth Engine". In this manuscript, researchers used the Google Earth Engine platform and freely-accessible Landsat imagery to create a yearly dataset (1985 through 2015) of surface coal mining in the Appalachian region of the United States of America.</div><div><br></div><div>This specific dataset is a GeoTIFF file depicting when an area was most recently mined, from the period 1985 through 2015. The raster values depict the year that mining was most recently detected by the paper's processing model. A year of "1984" indicates mining that likely was most recently mined at some point prior to 1985. These pre-1985 mining data are derived from a prior study; see https://skytruth.org/wp/wp-content/uploads/2017/03/SkyTruth-MTR-methodology.pdf for more information. This dataset does not indicate for how long an area was a mine or when mining began in a given area.</div

Available processing scripts and data products for download.

<p>Available processing scripts and data products for download.</p

Mapping the yearly extent of surface coal mining in Central Appalachia using Landsat and Google Earth Engine – Study Area (KML)

<p>These data accompany the 2018 manuscript published in <i>PLOS One</i> titled "Mapping the yearly extent of surface coal mining in Central Appalachia using Landsat and Google Earth Engine". In this manuscript, researchers used the Google Earth Engine platform and freely-accessible Landsat imagery to create a yearly dataset (1985 through 2015) of surface coal mining in the Appalachian region of the United States of America. <br></p> <p><br></p> <p>This specific dataset is a KML file of the study area. </p