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Most of the proposed solutions are based on the use of an embarrassingly parallel approach to run several instances of simulation of the model at a time, maintaining the simulation algorithm sequential.
Both methods have a median of 8/10, but we find more instances of simulations for which only 2 5 dysregulated genes were identified with inference.
We include several task sequences where we first ask students to make a prediction about the outcome of a particular instance of the simulation (after changing one of the initial conditions), then instruct them to run the simulation, observe the outcome, and then to respond to a reflection question.
Upon receipt of a request to execute a simulation, the middleware sets up the proper system environment and then launches an instance of the simulation engine with the appropriate configuration and parameters according to the instructions received from the client.
We ran 10 MCMC iterations for ABC model selections and checked convergence by running five instances of each simulation for different random number seeds.
This is done by highlighting in a graph structure the implicit production-consumption relationship between reaction instances of the simulation trajectory.
Each experimental observation is compared to a simulated outcome involving the same measurement protocol so, for example, a heteroplasmy variance estimate of 0.01 derived from a sample of 20 cells is compared to 20 instances of stochastic simulation.
By using the unique identifiers of the species in a reaction trajectory, which implicitly indicate the production-consumption relationship between reaction instances of the simulation, we construct a directed graph structure.
However, this approach is known to add numerical strength to the solution, which can cause potential problems in certain types of simulations, for instance in simulations of various instabilities.
DMD is an instance of discrete event simulation and so is difficult to scale: even in this multi-core era, all reported DMD codes are serial.
Other studies confirm the power of games and simulations in developing cognition abilities, especially in the instances of virtual simulations enhancing complex cognitive skills (Helle et al., 2011; Siewiorek, 2013), such as self-assessment (Arias Aranda, 2010), or higher-order thinking (Crocco et al., 2016).
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