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Since, the response modification factor depends on ductility and overstrength, the static nonlinear analysis has been performed on building models including single and double bracing bays, multi-floors and different brace configurations (chevron V, invert V and X bracing).
The thermal history of the fuel particles has been characterized by coupling particle image velocimetry (PIV) and pyrometry measurements so as to be integrated in empirical and phenomenological models including single rate, two-competing rate, distributed activation energy (DAE) and chemical percolation devolatilization (CPD) ones.
When data were examined within each site separately, screen scores performed better in sites with high base rates and models including single teacher screens accurately predicted convictions.
Table 2 illustrates the prediction accuracy of different feature classes and different learning models, including single task learning model and multitask learning model.
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Fundamental models include single and multiple server Markov queues, bulk arrival and bulk service processes, and priority queues.
The model framework includes single and double negative exponential decay functions that together produce net peat accumulation or degradation and hence carbon balance.
The degenerated case of the model includes single winding angle FW cylinder, FW vessel with a liner, and sandwich pipe with one or multiple core layers.
We describe in detail the learning mechanisms of these classifiers and different types of model architectures including single model architectures (SM) and multi-model architectures (MM).
Both methods can be applied with different model architectures, including single model (SM) with class labels as consequents, classification hyper-planes as consequents, and multi-model (MM) architecture.
We then constructed similar models including a single neighborhood level factor.
Models including the single risk score as a predictor provided an overall fit similar to the full Table 2 model.
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