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In this section, we show the computational results of three numerical examples to assess the performance of the proposed approach.
The effectiveness of the stabilization procedure is illustrated along with "patch test" examples to assess the consistency of the approximation.
Here, we provide numerical examples to assess the performance of the IEEE 802.16 uplink nrtPS service flow evaluated with the considered analytical model.
Finally, we provide some numerical examples to assess the performance of the proposed approaches (max-rate and min-power) in the multiuser case.
The effectiveness of the formulation and code implementation is illustrated through "patch tests" along with other examples to assess this approximation.
In this section, two numerical examples to assess the feasibility and effectiveness of Algorithms 3.1 3.2 in terms of iteration number (denoted by "IT"), computing time (in seconds, denoted by "CPU"), and the relative error (denoted as "Error") are provided.
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The method provides an original variable's importance index for classification and regression that can be applied in other fields [ 14] for example, to assess the RS in the reprioritization component of QoL assessments.
The state allows the providers, for example, to assess how many hours of care a week each developmentally disabled person's condition warrants.
Finally, we present a numerical example to assess and compare the communication cost with several gossip-based algorithms to achieve a given performance.
For example, to assess sadness, they could choose either: "I do not feel sad", "I feel sad much of the time", "I am sad all the time", or "I am so sad or unhappy that I cannot stand it".
For example, to assess sadness, patients can choose either: "I do not feel sad", "I feel sad much of the time", "I am sad all the time", or "I am so sad or unhappy that I can't stand it".
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