A general EEP algorithm simply utilizes the order of the embedded substreams as prioritarization information.
On the other hand, with a virtual GPU on which all blocks are active simultaneously (totally utilized), the order of the algorithm would be O(R) as the threads are independent from each other, not counting the synchronization points.
Only one paper [8] utilizes the order-α estimator that will be utilized to estimate all efficiency results in this paper.
The proposed PPCpred method, which utilizes the order-based aggregation of the propensities predicted by the four SVM models, was evaluated on the test datasets for the predictions of each of the four outcomes, i.e. prediction of material production, purification, crystallization and diffraction-quality crystallization, as well as for the 4-class prediction.
Furthermore, five ANFIS models with 6, 9, 19, 21 and 51 rules were developed utilizing the first order Sugeno fuzzy approach by back-propagation neural networks training algorithm.
We study discretizations that include a fully conforming approach as well as mixed finite element formulations utilizing the lowest order Nédélec edge elements (in 2D) on rectangular grids.
The standardised disequilibrium coefficient (D') was calculated for each pair of FCGRs, utilizing the gene order derived from our electronic contig.
Utilizing the first-order condition of the above profit, the optimal order quantity is determined.
For the estimated value (hat{nu }_{text{s}}), the term ({mathcal{G}}left( t right)) demands the information of (mathop {mu_{text{a}} }limits^) and (dot{nu }_{text{s}}), which can be retrieved by utilizing the second-order sliding mode observer.
By utilizing the second-order sparse expression (13) and the single-channel sampling model (9), the single-channel sampling vector can be rewritten as begin{array}rcl@ {textbf{Y}_{1 M}} = {boldsymbol{bar Phi bar Psi chi }} + {boldsymbol{sigma }} end{array} (14).
