Motivation: Microarray experiments are affected by numerous sources of non-biological variation that contribute systematic bias to the resulting data. In a dual-label (two-color) cDNA or long-oligonucleotide microarray, these systematic biases are often manifested as an imbalance of measured fluorescent intensities corresponding to Sample A versus those corresponding to Sample B. Systematic biases also affect between-slide comparisons. Making effective

Artificial neural networks (ANN) aided with dimensional analysis have been successfully applied in multiphase reactors modeling when considerable amount of experimental data (or database) is available. An important problem that stemmed from this approach was the ambiguity to select the fittest combination of dimensionless numbers to be used as ANN inputs to predict a variable of interest. A genetic algorithm (GA) based methodology was proposed to

To increase confidence in neural network modeling of multiphase reactor characteristics, we have to take advantage of some a priori knowledge of the physical laws governing these systems in order to build neural models having phenomenological consistency (PC). A common form of PC is the monotonicity constraint of a characteristic to be modeled with respect to some important dimensional variables describing the multiphase system. When the inputs of

Numerous investigations have shown that artificial neural networks (ANN) can be successful for correlating experimental data sets of macroscopic multiphase flow characteristics, e.g., hold-up, pressure drop, interfacial mass transfer. The approach proved its worth especially when rigorous fluid mechanics treatment based on the solution of first-principle equations is not tractable. One perennial obstacle facing correlations is the choice of low-dimensionality