Several self-assembly approaches have flourished throughout the years, with specific features in terms of complexity of the implementation, sensitivity to process parameters, characteristics of the final colloidal assembly.
Furthermore, while all use BLAST-based statistical distance metrics for the clustering, the implementation, sensitivity, the notion of the distance metrics and consequently the depth of the hierarchical representations are different for each of the underlying algorithm and resource (Liu and Rost, 2003).
With regard to the "max accuracy" implementation, both sensitivity (87%) and accuracy (82%) are improved (respectively: +3% and +1%), with respect to the original SVM classifier, by the removal of 16% of FNs.
In our implementation, the sensitivity matrix requires most of the GPU memory.
Although simply a binary binding assay, the ease of implementation and sensitivity of this approach combined with the relatively low amounts of protein needed make this a powerful primary assay for ligand screening and for hit validation.
Theoretical analysis and numerical simulation indicate that the proposed new scheme achieves several desirable features such as high flexibility, good statistical properties, parallel implementation, high key sensitivity, and message sensitivity.
The practical implementation of the sensitivity analysis in a finite element code employing the Analytical, Semi-Analytical and Refined Semi-Analytical approaches is discussed in detail.
Then by numerical simulations one compares the performance of both control strategies, under criteria as transient response, steady-state accuracy, feasibility of control implementation and parameter sensitivity.
The method features (i) commonly used specimen geometry, (ii) straightforward experimental implementation, and (iii) sensitivity to relevant design parameters including PCM volume fraction, enthalpy of phase change, composite effective thermal conductivity, and specimen dimensions.
In summary, so as to specify the relative importance of each factor on the model response, the implementation of a sensitivity analysis is mandatory, while in the RSM model, the relative importance of each input parameter can be determined simply by looking at the coefficient of each factor in the model equation.
With its relative ease of implementation and greater sensitivity, WT-Pico is the preferred method of amplification for cases in which sample RNA is limiting.
