Accessing Healthy Food: A sentinel mapping study of healthy food retailing in Scotland

This study on the availability of an affordable healthy food shopping basket was commissioned by the Food Standards Agency Scotland and undertaken between 2005 and 2007 by the Centre for the Study of Retailing in Scotland

Species Visitation at Quail Feeders and Guzzlers in Southern New Mexico

Providing supplemental feed and water are sometimes used to manage scaled quail (Callipepla squamata) in the Chihuahuan Desert even though their biological and economical efficacies are questionable. Seasonal visitation rates of scaled quail and various nontarget species are important parameters affecting the efficacy of feeding and watering practices. However, empirical data on visitation by scaled quail at feeders and guzzlers are lacking. We used video surveillance to assess species visitation at free-choice quail feeders and guzzlers in south-central New Mexico during 2002. Scaled quail accounted for 19.4 and 21.5% of visitations at feeders and guzzlers, respectively. Mourning doves (Zenaida macroura), nongame birds, and desert cottontails (Sylvilagus audubonni) were the primary nontarget consumers at this site. Relative to similar studies of feeder visitation by northern bobwhite (Colinus virginianus) in west Texas, quail feeders tended to be more efficacious (i.e., a greater proportion of the feeder visitations were by quail) in this study. While the biological impacts of feeders and guzzlers remain poorly documented, their use by scaled quail suggests they are important foci within the birds’ home ranges. Video surveillance technology permits managers to make data-based decisions on the biological and economic worth of such management efforts. We also describe novel uses for video surveillance relative to facilitating reconnaissance of radiotagged quail whose radios had malfunctioned. Future research should assess the potential for using video surveillance at guzzlers to estimate chick survival in scaled quail

Perspective: Web-based machine learning models for real-time screening of thermoelectric materials properties

The experimental search for new thermoelectric materials remains largely confined to a limited set of successful chemical and structural families, such as chalcogenides, skutterudites, and Zintl phases. In principle, computational tools such as density functional theory (DFT) offer the possibility of rationally guiding experimental synthesis efforts toward very different chemistries. However, in practice, predicting thermoelectric properties from first principles remains a challenging endeavor [J. Carrete et al., Phys. Rev. X 4, 011019 (2014)], and experimental researchers generally do not directly use computation to drive their own synthesis efforts. To bridge this practical gap between experimental needs and computational tools, we report an open machine learning-based recommendation engine (http://thermoelectrics.citrination.com) for materials researchers that suggests promising new thermoelectric compositions based on pre-screening about 25 000 known materials and also evaluates the feasibility of user-designed compounds. We show this engine can identify interesting chemistries very different from known thermoelectrics. Specifically, we describe the experimental characterization of one example set of compounds derived from our engine, RE12Co5Bi (RE = Gd, Er), which exhibits surprising thermoelectric performance given its unprecedentedly high loading with metallic d and f block elements and warrants further investigation as a new thermoelectric material platform. We show that our engine predicts this family of materials to have low thermal and high electrical conductivities, but modest Seebeck coefficient, all of which are confirmed experimentally. We note that the engine also predicts materials that may simultaneously optimize all three properties entering into zT; we selected RE12Co5Bi for this study due to its interesting chemical composition and known facile synthesis.We thank the National Science Foundation for support of this research through NSF-DMR 1121053, as well as the Natural Sciences and Engineering Research Council of Canada (NSERC), and the DARPA SIMPLEX program N66001-15-C-4036. Additionally, this research made extensive use of shared experimental facilities of the Materials Research Laboratory: a NSF MRSEC, supported by NSF-DMR 1121053. MWG is thankful for support from NSERC through a Postgraduate Scholarship, support from the US Department of State through an International Fulbright Science & Technology Award, and support from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska–Curie grant agreement No. 659764. BM and GJM are founders and significant shareholders in Citrine Informatics Inc

A data-driven review of thermoelectric materials: Performance and resource considerations

In this review, we describe the creation of a large database of thermoelectric materials prepared by abstracting information from over 100 publications. The database has over 18,000 data points from multiple classes of compounds, whose relevant properties have been measured at several temperatures. Appropriate visualization of the data immediately allows certain insights to be gained with regard to the property space of plausible thermoelectric materials. Of particular note is that any candidate material needs to display an electrical resistivity value that is close to 1 mΩcm at 300 K, i.e., samples should be significantly more conductive than the Mott minimum metallic conductivity. The Herfindahl-Hirschman index, a commonly accepted measure of market concentration, has been calculated from geological data (known elemental reserves) and geopolitical data (elemental production) for much of the periodic table. The visualization strategy employed here allows rapid sorting of thermoelectric compositions with respect to important issues of elemental scarcity and supply risk.Engineering and Applied Science