For this reason, we utilized a prediction method which selectively chooses the most informative sample period, in order to predict incidence/mortality ASR-Ws for the years between 2010 and 2014 [ 15].
For example, Pelander et al. utilized a prediction tool for biotransformation in conjunction with a prediction tool for ion fragmentation (ACD/MS Fragmenter) to discriminate between isobaric precursor ions of quetiapine products in human urine [ 10].
To identify the nine-amino-acid transactivation domain (9aaTAD), we utilized a prediction tool from http://www.med.muni.cz/9aaTAD/index.php (last accessed April 30 , 2014, using the "moderately stringent pattern" option (Piskacek et al. 2007).
The LEMPC algorithms proposed here utilize a shrinking prediction horizon with respect to fixed (but potentially large) operation period to ensure improved performance, measured by the desired economic cost, over conventional LMPC by solving auxiliary LMPC problems and incorporating appropriate constraints, based on the LMPC solution, in their formulations at various sampling times.
To further the practicability of this method, we utilize a dynamic grey prediction, which shows high accuracy for decision making.
In addition, our results show that the proposed methods for integrating linguistic representation and utilizing a cascaded prediction strategy can further improve the accuracy of acoustic feature prediction.
We utilized a consensus prediction approach [30], based on the use of several available prediction methods including ARB (average relative binding), SMM (stabilized matrix method) and ANN (artificial neural network) algorithms available through the IEDB (Immune Epitope Database).
Our subsequent study will focus on the molecular mechanisms underlying these two functional SNPs utilizing a promoter prediction method and the experimental confirmation of our results in a cellular model of sepsis.
To derive the equation, we utilize a simplified and equivalent linear prediction ANC model and parameterize noise bandwidth using an autoregressive model.
A broker dynamically controls the number of resources using a threshold-based mechanism and utilizes a prediction system based on a machine learning approach to predict future workloads.
