Fig. 3 Evaluation of the gain-cost function for three examples showing different trends (using the physicochemical based descriptors models).
13321_2018_260_MOESM2_ESM.xlsx Additional file 2. Information about the applied datasets, performance of the predictive models, and evaluation of the gain- cost function for the different datasets and settings.
The modeling of conventional systems is also included to assist in the evaluation of the potential gain of these techniques.
It is necessary to identify these types of slip statistically based on space time seismicity patterns to allow empirical evaluation of the probability gain of large earthquakes.
However, the evaluation of the FD gain is extracted under unrealistic conditions, i.e., without considering the impact of the inter-cell interference and the traffic profile.
Evaluation of the heat gain and thermal load of the structure-taking into account occupants, lighting and appliances, is carried out.
A simple method for the filter design is presented which is aimed at overcoming the most serious drawback of advanced filtering algorithms for high dimensional systems related to very high computational cost in evaluation of the filter gain.
They also allow an evaluation of the power gain in models restricted to only the genuine effects compared to a more saturated model.
From the computational point of view, each evaluation of the information gain requires the calculation of the posterior, which in turn requires the integral solutions of the systems of ODEs.
