Elemental Abundance Ratios in Stars of the Outer Galactic Disk. II. Field Red Giants

We summarize a selection process to identify red giants in the direction of the southern warp of the Galactic disk, employing VI_C photometry and multi-object spectroscopy. We also present results from follow-up high-resolution, high-S/N echelle spectroscopy of three field red giants, finding [Fe/H] values of about -0.5. The field stars, with Galactocentric distances estimated at 10 to 15 kpc, support the conclusion of Yong, Carney, & de Almeida (2005) that the Galactic metallicity gradient disappears beyond R_GC values of 10 to 12 kpc for the older stars and clusters of the outer disk. The field and cluster stars at such large distances show very similar abundance patterns, and, in particular, all show enhancements of the "alpha" elements O, Mg, Si, Ca, and Ti and the r-process element Eu. These results suggest that Type II supernovae have been significant contributors to star formation in the outer disk relative to Type Ia supernovae within the past few Gyrs. We also compare our results with those available for much younger objects. The limited results for the H II regions and B stars in the outer disk also suggest that the radial metallicity gradient in the outer disk is shallow or absent. The much more extensive results for Cepheids confirm these trends, and that the change in slope of the metallicity gradient may occur at a larger Galactocentric distance than for the older stars and clusters. However, the younger stars also show rising alpha element enhancements with increasing R_GC, at least beyond 12 kpc. These trends are consistent with the idea of a progressive growth in the size of the Galactic disk with time, and episodic enrichment by Type II supernovae as part of the disk's growth. [Abridged]Comment: Accepted for publication in A

Stochastic climate theory and modeling

Stochastic methods are a crucial area in contemporary climate research and are increasingly being used in comprehensive weather and climate prediction models as well as reduced order climate models. Stochastic methods are used as subgrid-scale parameterizations (SSPs) as well as for model error representation, uncertainty quantification, data assimilation, and ensemble prediction. The need to use stochastic approaches in weather and climate models arises because we still cannot resolve all necessary processes and scales in comprehensive numerical weather and climate prediction models. In many practical applications one is mainly interested in the largest and potentially predictable scales and not necessarily in the small and fast scales. For instance, reduced order models can simulate and predict large-scale modes. Statistical mechanics and dynamical systems theory suggest that in reduced order models the impact of unresolved degrees of freedom can be represented by suitable combinations of deterministic and stochastic components and non-Markovian (memory) terms. Stochastic approaches in numerical weather and climate prediction models also lead to the reduction of model biases. Hence, there is a clear need for systematic stochastic approaches in weather and climate modeling. In this review, we present evidence for stochastic effects in laboratory experiments. Then we provide an overview of stochastic climate theory from an applied mathematics perspective. We also survey the current use of stochastic methods in comprehensive weather and climate prediction models and show that stochastic parameterizations have the potential to remedy many of the current biases in these comprehensive models

Disorientating, fun or meaningful? Disadvantaged families' experiences of a science museum visit

It is widely agreed that there is a need to increase and widen science partici- pation. Informal science learning environments (ISLEs), such as science museums, may provide valuable spaces within which to engage visitors—yet the visitor profile of science museums remains narrow. This paper seeks to understand the experiences of socially disadvantaged families within such spaces. Using a Bourdieusian analytic lens, we analyse qualitative data from a small study conducted with ten parents and ten children from an urban school who visited a large science museum. Data includes pre- and post-interviews, audio recordings and visit fieldnotes. We characterised families’ experiences as falling into three discourses, as ‘disorientating’, ‘fun’ or ‘meaningful’ visits. Analysis identifies how the families’ experiences, and the likelihood of deriving science learning from the visit, were shaped through interactions of habitus and capital. Implications for improving equity and inclusion within ISLEs are discussed

PHYSIOLOGICAL RESISTANCE OF COMMON BEAN LINES TO \u3ci\u3eSclerotinia sclerotiorum\u3c/i\u3e

INTRODUCTION. White mold (WM), caused by the fungus Sclerotinia sclerotiorum, is a serious constraint of common bean during the fall-winter season in Brazil. The most commonly used control measure is fungicide application. However, the high cost and the potentially deleterious effects on human health and environment have motivated the search for new options of WM management. Genetic resistance is a key component of the WM management, because it is easier for farmers to adopt and is environmentally safe. Since 2008, we have screened common bean lines/cultivars for WM resistance from the field trials named Value for Cultivation and Use (VCU) conducted under WM pressure. The lines screened in these trials have been developed by Federal University of Viçosa, Federal University of Lavras, EPAMIG and Embrapa Rice and Beans. Beginning in 2015, we have evaluated the genotypes screened in the VCU trials in comparison to three WM-resistant checks (A195, G122, and Cornell 605) in advanced field trials, straw and/or detached leaflet tests. Here, we present results from the straw and the detached leaflet tests to assess physiological resistance of lines/cultivars originally screened for resistance to foliar diseases and high yield

Simulation model for particle dynamics in rumen of cattle fed sugarcane diet

Animal simulation models are sets of equations used to describe biological processes. A non-steady state simulation model of cattle digestion is presented in order to represent nutrient availability as a response to feed intake pattern and the kinetics of particle size reduction. Variables representing the particle size reduction and discontinuous voluntary feed intake were included in a mechanistic model created to optimize the supplementation of sugarcane based diets. In general the predicted values were very close to observed values for fibre and nitrogen flows. The model has not shown consistent bias in relation to the behavior of the observed data of duodenal flow of neutral detergent fiber and non-ammonia nitrogen. Milk production simulations were quite close to actual values. Predictions were improved by the non steady-state model, taking into account variable intake rate in relation to the previous steady-state model. The model can be used to select strategies for supplementation of cattle fed sugarcane based diet