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Collisions are handled using a soft-sphere model.
Particle collisions are handled using a soft-sphere model that has been modified for parallel efficiency.
Patches that do not belong to any trained category are handled using a support vector machine (SVM) based classifier.
Thanks to GAs, the comfort and road holding are handled using a single high level parameter and illustrated via the Pareto optimality.
In the suggested method to prevent complexity, GA is used to find the best configuration while continuous variables are handled using a quasi LP formulation.
The problem-specific constraints are handled using a modified feasible-based mechanism, and the harmony search scheme is employed to deal with variable constraints.
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These value modifications are handled using an extensible collection of value_translation tags.
The case of repeated eigenvalues is handled using a dual formulation.
The uncertain nature of the network is handled using a stochastic-possibilistic programming approach.
The stiff source term in the reactive case is handled using a Runge Kutta error-control scheme.
In particular, interventions can be handled using a possibilistic counterpart of Jeffrey's rule of conditioning under uncertain inputs.
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