The Challenge of Integrating Faith-Learning-Living in Teacher Education

Teacher educators from member institutions of the Coalition for Christian Colleges and Universities are currently challenged in an unprecedented way. The challenge is to satisfy increasingly rigorous state and national teacher education standards and to fulfill the commonly held mission of Coalition institutions to integrate faith-learning-living. The research presented in this article traces the long history of integration and presents various theoretical integration models commonly supported by educators at Christian colleges and universities. This article suggests meeting the challenge in part through an original six component integration model with potential value for Christian educators representing various academic disciplines

Hot-wire and hot-film anemometry

The circuit techniques, electronics, dynamic properties, and the applications of the anemometers are given

Origins of elastic properties in ordered nanocomposites

We predict a diblock copolymer melt in the lamellar phase with added spherical nanoparticles that have an affinity for one block to have a lower tensile modulus than a pure diblock copolymer system. This weakening is due to the swelling of the lamellar domain by nanoparticles and the displacement of polymer by elastically inert fillers. Despite the overall decrease in the tensile modulus of a polydomain sample, the shear modulus for a single domain increases dramatically

Suggesting Cooking Recipes Through Simulation and Bayesian Optimization

Cooking typically involves a plethora of decisions about ingredients and tools that need to be chosen in order to write a good cooking recipe. Cooking can be modelled in an optimization framework, as it involves a search space of ingredients, kitchen tools, cooking times or temperatures. If we model as an objective function the quality of the recipe, several problems arise. No analytical expression can model all the recipes, so no gradients are available. The objective function is subjective, in other words, it contains noise. Moreover, evaluations are expensive both in time and human resources. Bayesian Optimization (BO) emerges as an ideal methodology to tackle problems with these characteristics. In this paper, we propose a methodology to suggest recipe recommendations based on a Machine Learning (ML) model that fits real and simulated data and BO. We provide empirical evidence with two experiments that support the adequacy of the methodology

Automated Identification and Differentiation of Spectrally Similar Hydrothermal Minerals on Mars

Early telescopic observations corroborated hydration related absorptions on Mars in the infrared. Images from the Viking missions led to speculation of hydrothermal alteration and were followed by two missions which mapped the spatial variability of the ~ 3 m hydration feature. Since then, the Compact Reconnaissance Imager for Mars (CRISM) has provided high spatial resolution (up to 18m) spectral identification of a suite of hydrothermal and diagenetic minerals which have illuminated a range of formation mechanisms. Presence/absence and spatial segregation or mixing of minerals like prehnite, epidote, chlorite amphiboles, and mixed-layer Fe/Mg smectite-chlorite provide valuable evidence for the geologic setting of deposits on Earth, and these phases are often used as temperature and aqueous chemistry indicators in terrestrial systems. Mapping the distribution of these phases will help to answer whether Mars had widespread conditions favorable for low-grade metamorphism and diagenesis, or only focused hydrothermal systems in areas of high heat flow. Further characterizing the chemistry and structure of these phases will then help to answer how most of the widespread Fe/Mg phyllosilicates formed, further defining early geochemical cycling and climate. A fully automated approach for accurate mapping of important hydrothermal mineral phases on Mars has been a challenge. Due to overlapping features in the M-OH region (~2.2-2.4 m), the strongest absorption features of chlorite, prehnite, and epidote in the short-wave infrared are difficult to distinguish from one another and from the most commonly occurring hydrated silicates on Mars, Fe/Mg smectites. Weaker absorptions are present in both prehnite and epidote which help to distinguish them from chlorite and smectites, but their relative strength in the presence of noise and spatial mixing is often too low to confidently identify them without the noise suppression and feature enhancement methods described here. The spectral signatures of mixed-layer Fe/Mg smectite-chlorite and partially chloritized Fe/Mg smectites have not yet been adequately assessed. Here we evaluate the effectiveness of two empirical and statistical methods for identifying and differentiating these phases using CRISM data

The contribution of grass and clover root turnover to N leaching

Sources of inorganic and organic N leaching from grass-clover mixtures at field sites in Denmark, Germany and Iceland were investigated. Grass or clover was labelled with 15N-urea four times (autumn 2007, spring, summer and autumn 2008) prior to the leaching season in autumn and winter 2008. Soil water was sampled at 30 cm depth and analyzed for 15N-enrichment of dissolved inorganic N (DIN) and dissolved organic N (DON). Most 15N was recovered in DON for both labelled grass and clover at all sites. At the Danish site, grass and clover contributed more to the DON pool than the DIN whereas the opposite was observed at the German and Icelandic sites. The results show that both clover and grass contribute directly to N leaching from the root zone in mixtures, and that clover contribution is higher than grass. Furthermore, the present study indicates that roots active in the growth season prior to the drainage period contribute more to N leaching than roots active in the growth season the previous year, which is consistent with estimates of root longevity at the three sites