Temperature dependent model dielectric function of highly disordered Ga0.52In0.48P

2011 ◽  
Vol 519 (9) ◽  
pp. 2859-2862
Author(s):  
E. Montgomery ◽  
C. Krahmer ◽  
K. Streubel ◽  
T. Hofmann ◽  
E. Schubert ◽  
...  
Keyword(s):  
Dielectric Function ◽  
Temperature Dependent ◽  
Dependent Model ◽  
Temperature Dependent Model

scholarly journals Temperature-Dependent Model for Small-Strain Shear Modulus of Unsaturated Soils

2020 ◽  
Vol 146 (12) ◽  
pp. 04020136
Author(s):  
Farshid Vahedifard ◽  
Sannith Kumar Thota ◽  
Toan Duc Cao ◽  
Radhavi Abeysiridara Samarakoon ◽  
John S. McCartney
Keyword(s):  
Shear Modulus ◽  
Unsaturated Soils ◽  
Small Strain ◽  
Temperature Dependent ◽  
Small Strain Shear Modulus ◽  
Dependent Model ◽  
Temperature Dependent Model

Predicting Flowering of Rhizome Johnsongrass (Sorghum halepense) Populations Using a Temperature-dependent Model

Weed Science ◽  
1996 ◽  
Vol 44 (2) ◽  
pp. 266-272 ◽  
Author(s):  
David L. Holshouser ◽  
James M. Chandler
Keyword(s):  
Rate Equation ◽  
Model Performance ◽  
Population Level ◽  
Weibull Function ◽  
Data Sets ◽  
Temperature Dependent ◽  
Independent Field ◽  
Dependent Model ◽  
Improved Model ◽  
Temperature Dependent Model

Research was conducted to formulate a temperature-dependent population-level model for rhizome johnsongrass flowering. A nonlinear poikilotherm rate equation was used to describe development as a function of temperature and a temperature-independent Weibull function was used to distribute development times for the population. Johnsongrass flowering data were collected under constant temperature conditions to parameterize the poikilotherm rate equation and Weibull function. Coupling the poikilotherm rate equation with the Weibull function resulted in a population level temperature-dependent model. The model was validated against independent field data sets. The model accurately predicted rhizome johnsongrass flowering from plants emerging in the spring. The model performed poorly for plants emerging in summer. Adjustments to the high-temperature inhibition parameter of the poikilotherm rate equation improved model performance in the summer without affecting spring predictions.

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