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Full-Text Articles in Life Sciences
Toward “Optimal” Integration Of Terrestrial Biosphere Models, Christopher R. Schwalm, Deborah N. Huntzinger, Joshua B. Fisher, Anna M. Michalak, Kevin Bowman, Philippe Ciais, Robert Cook, Bassil El-Masri, Daniel Hayes, Maoyi Huang, Akihiko Ito, Atul Jain, Anthony W. King, Hiumin Lei, Junjie Liu, Chaoqun (Crystal) Lu, Jaifu Mao, Shushi Peng, Benjamin Poulter, Daniel Ricciuto, Kevin Schaefer, Xiaoying Shi, Bo Tao, Hanqin Tian, Weile Wang, Yaxing Wei, Jia Yang, Ning Zeng
Toward “Optimal” Integration Of Terrestrial Biosphere Models, Christopher R. Schwalm, Deborah N. Huntzinger, Joshua B. Fisher, Anna M. Michalak, Kevin Bowman, Philippe Ciais, Robert Cook, Bassil El-Masri, Daniel Hayes, Maoyi Huang, Akihiko Ito, Atul Jain, Anthony W. King, Hiumin Lei, Junjie Liu, Chaoqun (Crystal) Lu, Jaifu Mao, Shushi Peng, Benjamin Poulter, Daniel Ricciuto, Kevin Schaefer, Xiaoying Shi, Bo Tao, Hanqin Tian, Weile Wang, Yaxing Wei, Jia Yang, Ning Zeng
Chaoqun (Crystal) Lu
Multimodel ensembles (MME) are commonplace in Earth system modeling. Here we perform MME integration using a 10-member ensemble of terrestrial biosphere models (TBMs) from the Multiscale synthesis and Terrestrial Model Intercomparison Project (MsTMIP). We contrast optimal (skill based for present-day carbon cycling) versus naïve (“one model-one vote”) integration. MsTMIP optimal and naïve mean land sink strength estimates (−1.16 versus −1.15 Pg C per annum respectively) are statistically indistinguishable. This holds also for grid cell values and extends to gross uptake, biomass, and net ecosystem productivity. TBM skill is similarly indistinguishable. The added complexity of skill-based integration does not materially change …
Artificial Neural Network Modeling Of Ddgs Flowability With Varying Process And Storage Parameters, Rumela Bhadra, K. Muthukumarappan, Kurt A. Rosentrater
Artificial Neural Network Modeling Of Ddgs Flowability With Varying Process And Storage Parameters, Rumela Bhadra, K. Muthukumarappan, Kurt A. Rosentrater
Kurt A. Rosentrater
Neural Network (NN) modeling techniques were used to predict flowability behavior of distillers dried grains with solubles (DDGS) prepared with varying condensed distillers soluble (10, 15, and 20%, wb), drying temperature (100, 200, and 300°C), cooling temperature (-12, 0, and 35°C) and cooling time (0 and 1 month) levels. Response variables were selected based on our previous research results, and included aerated bulk density, Hausner Ratio, angle of repose, Total Flowability Index, and Jenike Flow Function. Various neural network models were developed using multiple input variables in order to predict single response variables or multiple response variables simultaneously. The NN …
Single-Screw Extrusion Modeling Effects On Extrusion Processing Parameters And Physical Properties Of Ddgs-Based Nile Tilapia ( Oreochromis Niloticus ) Feeds, Ferouz Y. Ayadi, Parisa Fallahi, Kurt A. Rosentrater, Kasiviswanathan Muthukumarappan
Single-Screw Extrusion Modeling Effects On Extrusion Processing Parameters And Physical Properties Of Ddgs-Based Nile Tilapia ( Oreochromis Niloticus ) Feeds, Ferouz Y. Ayadi, Parisa Fallahi, Kurt A. Rosentrater, Kasiviswanathan Muthukumarappan
Kurt A. Rosentrater
A single-screw laboratory extruder was used to conduct an L 18 (2 2 X 3 6 ) Taguchi fractional factorial study of aquafeed processing. The ingredients were based on a formulation for nutritionally-balanced Nile tilapia diets containing distillers dried grains with solubles (DDGS) and soybean meal as the main protein sources, in addition to constant amounts of corn flour, whey, and fish meal. The effects of three levels of DDGS (20, 30 and 40%), soybean meal (30, 40 and 50%), ingredient moisture content (20, 30 and 40% db), screw speed (100, 150 and 200 rpm), barrel temperatures (80-100-100°C, 80-120-120°C and …
Neural Network And Regression Modeling Of Extrusion Processing Parameters And Properties Of Extrudates Containing Ddgs, Nehru Chevanan, Kasiviswanathan Muthukumarappan, Kurt A. Rosentrater
Neural Network And Regression Modeling Of Extrusion Processing Parameters And Properties Of Extrudates Containing Ddgs, Nehru Chevanan, Kasiviswanathan Muthukumarappan, Kurt A. Rosentrater
Kurt A. Rosentrater
Two sets of experiments using a single-screw extruder were conducted with an ingredient blend containing 40% DDGS (distillers dried grains with solubles), along with soy flour, corn flour, fish meal, vitamin mix, and mineral mix, with the net protein content adjusted to 28%. The variables controlled in the first experiment included seven levels of die size, three levels of moisture content, three levels of temperature gradient in the barrel, and one screw speed. The variables altered in the second experiment included three levels of moisture content, three levels of temperature gradient in the barrel, five levels of screw speed, and …