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    Modeling Scenario
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    7-008-MachineReplacement-ModelingScenario
    Students build an integro-differential equation model using a convolution for machine replacement strategies for two different machine failure models.
    Probabilityreplacement numberLaplace transformindustrial engineeringmachineconvolutiondistributiojstep functionderivative definition
    Modeling Scenario
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    1-116-TropicalStormWindspeeds-ModelingScenario
    We model the decay of tropical cyclone winds once a storm makes landfall. We use data from two recent storms from the National Hurricane Center to estimate parameters emanating from a differential equation using a first order exponential decay model.
    hurricanedecaytropical stormcyclones
    Modeling Scenario
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    1-046-GoingViral-ModelingScenario
    Students participate in a simulation of the spread of a viral disease in the classroom and model the process with a logistic differential equation. The simulation uses random numbers and the entire class participates.
    simulationmathematical modeldatalogisticrandom number generatorfittingrandom process
    Modeling Scenario
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    1-122-SpreadPEV-ModelingScenario
    We present data on world sales data of plug-in electric vehicles (PEVs) and request a model on the rate of change in sales over time, leading to prediction as to number of PEVs in the future.
    datalogisticpredictionspreadplug-in vehiclePEV
    Modeling Scenario
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    1-038-Ebola-ModelingScenario
    Students will use data published by the World Health Organization to model the 2014 outbreak of the Ebola virus in West Africa. We begin with a simple exponential growth model and move through the modeling process to the logistic growth model.
    population dynamicsdata analysisexponential growthEbolalogisticepidemic
    Modeling Scenario
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    6-016-PandemicModeling-ModelingScenario
    The recent coronavirus outbreak has infected millions of people worldwide and spread to over 200 countries. How can we use differential equations to study the spread of coronavirus?
    population dynamicsinfectious diseasediseaseEbolaeSIR modelsCOVID19pandemicepidemicreproduction number
    Modeling Scenario
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    1-001c-PopulationDecayThenSome-ModelingScenario
    You will be modeling the following situation: 100 people are in a hotel. Each day, each person has a random chance of 50% of leaving the hotel. No new people enter the hotel.
    simulationoccupancypopulationhotel
    Modeling Scenario
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    1-047-Condensation-ModelingScenario
    We simulate the random motion of 200 particles in a 50 by 50 square in which a particle bounces off the two vertical and top walls and condenses on the bottom wall. Animations are produced and data is offered for modeling with a differential...
    simulationsum of square errorscondensationbest fit
    Modeling Scenario
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    1-115-ModelingWithFirstOrderODEs-ModelingScenario
    Several models using first order differential equations are offered with some questions on formulating a differential equations model with solutions provided.
    bacteriafalling objectNewton's Law of Coolingdrugcoolingdrag
    Modeling Scenario
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    1-002-Tossing-ModelingScenario
    We offer students simulation experience or data from a simulation and ask them to model the simulation using several approaches, to include exponential decay fit, difference equation, and differential equation.
    Modeling and simulationexponentialdecaydice
    Modeling Scenario
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    1-019-RocksInTheHead-Modeling Scenario
    We describe an experiment with data on the perception of the individual mass of a collection of rocks in comparison to a 100 g brass mass. Students use the logistic differential equation as a reasonable model and estimate parameters.
    logistic growthparameter estimationexperiment
    Modeling Scenario
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    6-005-InsectColonySurvivalOpt-ModelingScenario
    We present a system of nonlinear differential equations to model the control of energy flow into producing workers or reproducers in an insect colony, using a set of given parameters and a number of different energy functions.
    optimizationOptimal Control Theoryinsect colonyoptimal controlnatural resourcesPointryagin's Maximum Principlecontrol variable
    Technique Narrative
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    1-002-IntegratingFactor-TechniqueNarrative
    We develop a strategy to solve first order differential equations by transforming one side of the equation to the derivative of a product of two functions, thereby making it easy to antidifferentiate that side.
    Integrating Factor
    Modeling Scenario
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    1-134-LanguageDynamics-ModelingScenario
    Students will be introduced to a mathematical model for language dynamics. Specifically, the model describes the change in the fraction of a population speaking one language over another.
    languagecompartmentphase linesbifurcationswitching
    Modeling Scenario
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    1-135-FishHarvesting-ModelingScenario
    This short activity will walk students through a guided list of questions to help them to understand how the stability of equilibrium changes with changes in a model parameter, in this case the rate of harvesting fish.
    population dynamicsfishstabilitybifurcationharvest
    Modeling Scenario
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    3-031-SpringCost-ModelingScenario
    We assume students are familiar with overdamping and underdamping of a spring-mass-dashpot system. Students will apply this knowledge to model the interplay between spring constant, tolerance, and cost.
    designunit costspring constantstatic equilibriumspring masss dashpot
    Modeling Scenario
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    1-109-EmployeeAttrition-ModelingScenario
    This scenario models the loss of employees and the employer's attempt to retain them through stock options. It most naturally is solved with a first-order linear decay model with two populations.
    populationlinear growthdecayemployeeattrition
    Modeling Scenario
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    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.
    VarianceMeanstochasticrandomoperations researchindustrial engineeringPoisson processtraffic
    Modeling Scenario
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    6-021-AcornsRodentsSnakes-ModelingScenario
    Students are given the opportunity to build a three tropic level model in which snakes eat rodents, rodents eat acorns, and acorns grow and occasionally mast.
    predationforestsnakesrodentsacornstrophic levelmasting
    Modeling Scenario
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    1-005B-ChemDataCollection-ModelingScenario
    A chemistry graduate student is collecting data on a chemical reaction and does a poor job of collecting the data. Indeed, he only collects the data at the start and end of a number of three minute intervals and does not note the times.
    kineticslaboratoryorder of reactionchemical reactionchemical datainfusion