An Assessment of the India Soy Protein Market

This research is a first step in determining India's future need for soy-based protein products. The objective of this study is to determine India's protein demand over the next ten years. Then, using the per capita protein demand derived from this study, along with income, population, and dietary information, per capita soy protein consumption was estimated for the same time period. It was found that income growth has a large positive affect on protein consumption.Demand and Price Analysis, Food Consumption/Nutrition/Food Safety,

Cross-Hedging Distillers Dried Grains: Exploring Corn and Soybean Meal Futures Contracts

Ethanol mandates and high fuel prices have led to an increase in the number of ethanol plants in the U.S. in recent years. In turn, this has led to an increase in the production of distillers dried grains (DDGs) as a co-product of ethanol production. DDG production in 2006 is estimated to be near 11 million tons. A sharp increase in ethanol production and thus DDGs is expected in 2007 with an increase with the number of ethanol plants. As with most competitive industries, there is some level of price risk in handling DDGs and no futures contract available for this co-product. Ethanol plants, as well as users of DDGs, may find cross-hedging DDGs with corn or soybean meal (SBM) futures as an effective means of managing risk. Traditionally, DDGs are hedged using only corn futures.

Cross-Hedging Distillers Dried Grains Using Corn and Soybean Meal Futures Contracts

Ethanol mandates have led to an increase in the production of distillers dried grains (DDGs), a co-product of ethanol production that is incorporated into livestock rations. As with most competitive industries, there is some level of price risk in handling DDGs, and there is no DDG futures contract available for managing price risk. Commonly, DDGs are hedged using only corn futures. Our results suggest that cross-hedge risk may be reduced by including soybean meal futures in an encompassing cross-hedge strategy. Further, we also conclude soybean meal futures currently may be slightly more effective at reducing risk than in the past.cross-hedge, distillers dried grains, ethanol, price risk, Agribusiness, Demand and Price Analysis,

Skeletal adaptations in young male mice after 4 weeks aboard the International Space Station

Gravity has an important role in both the development and maintenance of bone mass. This is most evident in the rapid and intense bone loss observed in both humans and animals exposed to extended periods of microgravity in spaceflight. Here, cohabitating 9-week-old male C57BL/6 mice resided in spaceflight for ~4 weeks. A skeletal survey of these mice was compared to both habitat matched ground controls to determine the effects of microgravity and baseline samples in order to determine the effects of skeletal maturation on the resulting phenotype. We hypothesized that weight-bearing bones would experience an accelerated loss of bone mass compared to non-weight-bearing bones, and that spaceflight would also inhibit skeletal maturation in male mice. As expected, spaceflight had major negative effects on trabecular bone mass of the following weight-bearing bones: femur, tibia, and vertebrae. Interestingly, as opposed to the bone loss traditionally characterized for most weight-bearing skeletal compartments, the effects of spaceflight on the ribs and sternum resembled a failure to accumulate bone mass. Our study further adds to the insight that gravity has site-specific influences on the skeleton

Active Learning to Overcome Sample Selection Bias: Application to Photometric Variable Star Classification

Despite the great promise of machine-learning algorithms to classify and predict astrophysical parameters for the vast numbers of astrophysical sources and transients observed in large-scale surveys, the peculiarities of the training data often manifest as strongly biased predictions on the data of interest. Typically, training sets are derived from historical surveys of brighter, more nearby objects than those from more extensive, deeper surveys (testing data). This sample selection bias can cause catastrophic errors in predictions on the testing data because a) standard assumptions for machine-learned model selection procedures break down and b) dense regions of testing space might be completely devoid of training data. We explore possible remedies to sample selection bias, including importance weighting (IW), co-training (CT), and active learning (AL). We argue that AL---where the data whose inclusion in the training set would most improve predictions on the testing set are queried for manual follow-up---is an effective approach and is appropriate for many astronomical applications. For a variable star classification problem on a well-studied set of stars from Hipparcos and OGLE, AL is the optimal method in terms of error rate on the testing data, beating the off-the-shelf classifier by 3.4% and the other proposed methods by at least 3.0%. To aid with manual labeling of variable stars, we developed a web interface which allows for easy light curve visualization and querying of external databases. Finally, we apply active learning to classify variable stars in the ASAS survey, finding dramatic improvement in our agreement with the ACVS catalog, from 65.5% to 79.5%, and a significant increase in the classifier's average confidence for the testing set, from 14.6% to 42.9%, after a few AL iterations.Comment: 43 pages, 11 figures, submitted to Ap

Cross-Hedging Distillers Dried Grains: Exploring Corn and Soybean Meal Futures Contracts

Ethanol mandates and high fuel prices have led to an increase in the number of ethanol plants in the U.S. in recent years. In turn, this has led to an increase in the production of distillers dried grains (DDGs) as a co-product of ethanol production. DDG production in 2006 is estimated to be near 11 million tons. A sharp increase in ethanol production and thus DDGs is expected in 2007 with an increase with the number of ethanol plants. As with most competitive industries, there is some level of price risk in handling DDGs and no futures contract available for this co-product. Ethanol plants, as well as users of DDGs, may find cross-hedging DDGs with corn or soybean meal (SBM) futures as an effective means of managing risk. Traditionally, DDGs are hedged using only corn futures

An Assessment of the India Soy Protein Market

This research is a first step in determining India's future need for soy-based protein products. The objective of this study is to determine India's protein demand over the next ten years. Then, using the per capita protein demand derived from this study, along with income, population, and dietary information, per capita soy protein consumption was estimated for the same time period. It was found that income growth has a large positive affect on protein consumption

Cross-Hedging Distillers Dried Grains Using Corn and Soybean Meal Futures Contracts

Ethanol mandates have led to an increase in the production of distillers dried grains (DDGs), a co-product of ethanol production that is incorporated into livestock rations. As with most competitive industries, there is some level of price risk in handling DDGs, and there is no DDG futures contract available for managing price risk. Commonly, DDGs are hedged using only corn futures. Our results suggest that cross-hedge risk may be reduced by including soybean meal futures in an encompassing cross-hedge strategy. Further, we also conclude soybean meal futures currently may be slightly more effective at reducing risk than in the past