Due to the significant computation cost involved in the previously mentioned approaches, the researchers begin to pay more attention to the machine learning approach (e.g., the artificial neural network [13, 14], support vector machine [15], and fuzzy logic [16, 17]) to realize the fingerprint positioning.
Our study applied a 3D connectivity algorithm, similar to that of van den Heuvel and colleagues [ 10] (2006), whose results will clearly vary from the previously mentioned approaches.
PS release experiments can be performed using both of the previously mentioned approaches and additional parameters such as the presence of FCS in the culture medium can be tested for their influence on the release kinetics.
In comparison to the approaches previously mentioned, the approach introduced in this work overcomes their limitations by identification of the complete response function and carrying out the optimization in silico.
As previously mentioned, different approaches and dimensions exist to operationalize/conceptualize personal values and traits.
As previously mentioned, valid approaches for endurance testing in tennis should include a physical workload profile analysed during competition.
Taken together, the previously mentioned experimental approaches are suited to provide comprehensive information on the potential use of a particular and probably newly developed PS agent in the frame of PDT or PDI.
In contrast to the previously mentioned programs, our approach differs in two aspects: On the one hand, we ensure on the speed of the calculation by using an efficient dynamic programming approach with "memoization" [13], i.e. storing partial solutions and combining them to globally optimal solutions.
As previously mentioned, deciding which approach to use involves both statistical and substantive considerations.
