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Modeling Scenario
378
views
199
downloads
0
comments
1-016-DogDrugs-ModelingScenario
We offer a problem to determine the necessary drug administration in order to keep a dog sedated with specific information on half-life for an exponentially decaying presence of the drug in the body.
wild_dogs
drug discovery
half-life
pentobarbital sodium
anesthesia
Modeling Scenario
309
views
228
downloads
0
comments
1-085-DrugBolus-ModelingScenario
Given data on the concentration of a drug in the plasma of a human in mg/L at certain time intervals in hours can we determine the rate at which the drug leaves the plasma as well as the initial amount administered in a intravenous bolus of the drug?
concentration
sum of square errors
bolus
drug
rate constant
administer
Modeling Scenario
650
views
394
downloads
0
comments
5-011-ModelingIbuprofren-ModelingScenario
We consider modeling of data from a clinical experiment administered as oral doses of 400 mg ibuprofen, an analgesic pain reliever. Concentrations of ibuprofen in the serum/plasma of the subjects were recorded after the initial ingestion of the...
data
AIC
Akaike Information Criterion
absorption
sum of square errors
compartment model
ibuprofen
delay
Modeling Scenario
208
views
239
downloads
0
comments
5-020-ParacetamolAbsorption-ModelingScenario
We offer drug absorption data on paracetamol, an analgesic pain reliever, for two different sets of patients, vegetarian and non-vegetarian. Students build a two compartment model for plasma and non-plasma (tissue) compartments of human patients.
paracetamol
analgesic
vegetarian
non-vegetarian
Modeling Scenario
282
views
188
downloads
0
comments
5-023-FakingGause-ModelingScenario
We use a fake or toy data set to permit discovery of the parameters in a two population protozoan model used to study paramecium and yeast competition in the 1930's studies of G. F. Gause in the Soviet Union.
competition
logistic
yeast
Gause
paramecium
poplulation
Modeling Scenario
306
views
232
downloads
0
comments
1-027-StochasticProcesses-ModelingScenario
We build the infinite set of first order differential equations for modeling a stochastic process, the so-called birth and death equations. We will only need to use integrating factor solution strategy or DSolve in Mathematica for success.
Variance
Mean
stochastic
random
operations research
industrial engineering
Poisson process
traffic
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