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    Potential Scenario
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    2015-Carl_Lutzer-Using Expected Value to Introduce the Laplace Transform
    We propose an introduction to the Laplace transform in which Riemann sums are used to approximate the expected net change in a function, assuming that it quantifies a process that can terminate at random.
    Expected ValueRiemann sumnet change
    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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    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
    367

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    891

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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
    443

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    174

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    1-039-StochasticPopModels-ModelingScenario
    We develop strategies for creating a population model using some simple probabilistic assumptions. These assumptions lead to a system of differential equations for the probability that a system is in state (or population size) n at time t.
    Probabilitypopulation dynamicsVarianceMeanstochasticdeterministic
    Modeling Scenario
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    480

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    1-047a-CondensationOptimization-ModelingScenario
    We seek to optimize a condensation process which is modeled by a simulation using 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.
    simulationdataoptimizationenergycondensationbest fitcostsum of scquare errors
    Modeling Scenario
    351

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    156

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    1-001d-HotelPopulationDecay-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. Before you run the simulation in MATLAB, answer some questions.
    simulationlogisticexponential decayfitting
    Potential Scenario
    132

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    2014-Wang-EtAl-Estimating Mixed-Effects Differential Equation Models
    When multiple replicates of measurements are available for the dynamic process, it is of great interest to estimate mixed-effects in the ODE model for the process. We propose a semiparametric method to estimate mixed-effects ODE models.
    pharmacokineticsdynamic modelssmoothingsplinemixed-effects modelsHiV combination therapy
    Potential Scenario
    219

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    1999-Khorasheh-Ahmadi-Gerayeli-Application of Direct Search Optimization for Pharmacokinetic Parameter Estimation
    For simple pharmacokinetic compartmental models, analytical solution to the governing differential equations provide a mean to evaluate the associated rate constants. Such methods, however, can not be used used for more complex multi-compartment...
    sum of square errorsleast squarespharmacokineticslineteicscompartmental modelsdirect search
    Modeling Scenario
    172

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    138

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    6-003-SchoolFluEpidemic-ModelingScenario
    We offer a model of the spread of flu in a school dormitory and are asked to find when the flu levels reach their peak and explain long term behavior of the spread of the flu.
    infectionepidemicSIR modelflupeakschool children
    Modeling Scenario
    318

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    145

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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
    258

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    118

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    1-084-GoingViral-ModelingScenario
    Students employ randomization in order to create a simulation of the spread of a viral disease in a population (the classroom). Students then use qualitative analysis of the expected behavior of the virus to devise a logistic differential equation.
    simulationlogisticrandomfittingpartial fractions
    Potential Scenario
    140

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    23

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    2008-Duran-Caginalp-Parameter optimization for differential equations in asset price forecasting
    The optimization procedure is used in conjunction with daily market prices (MPs) and net asset values to determine the parameters for which the AFDE yield the best fit for the previous n days.
    financeinverse problemnumerical nonlinear optimizationasset flowfinancial market dynamicsmarket return prediction algorithm
    Modeling Scenario
    243

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    313

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    6-008-PursuitModels-Modeling Scenario
    Students are prompted to build systems of nonlinear differential equations to model pursuit-evader activities in which a pursuer attempts to follow, perhaps track down and come close to, an evader without knowledge of the evader's intentions, just...
    Potential Scenario
    194

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    60

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    2010-Jungck-EtAl-Mathematical Manipulative Models-In Defense of Beanbag Biology
    This paper offers up samples of projects from the Bio- QUEST Curriculum Consortium’s 24-yr experience of holding faculty development workshops for biology and mathematics educators.
    epidemiologysimulationinfectious diseasedisease transmissiondiseasein-class activitybean bag activity
    General Resource
    196

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    44

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    2003-Knorrenschild-Gross-Text Books on Mathematical Modeling in Biology
    Text Books on Mathematical Modeling in Biology Compiled from the Internet by Michael Knorrenschild,
    biologyMathematical Biologytextbooktext