The task is tricky, since the complexity of interdisciplinary research defies a single definition.
The development of safety critical systems becomes even harder since the complexity of these systems grows continuously.
Since the complexity of automation tasks is steadily increasing the development of control procedures requires high engineering efforts.
Understanding the performance of suboptimal policy levels is practically important, since the complexity and uncertainty of innovation make optimal policy magnitudes elusive in the real world.
Since the complexity of such constraints and the switching instants is unknown, it is difficult to solve the problem by standard optimization techniques.
Since the complexity of the experimental setup for evaluating a planar inclusion in a solid component, numerical modeling is always desirable.
Since the complexity of this determination makes it impossible to deal with patterns of length greater than 4, we propose a polynomial heuristic.
Since the complexity of peak patterns becomes a benefit rather than a drawback, Pattern Picker opens new opportunities in NMR experiment design.
This is not to say that everything can be deduced mathematically from a small number of fundamental principles, since the complexity of real things defeats the power of mathematics or of the largest computers.
This is particularly helpful when applied to fermentation data, since the complexity of the reaction network in many cases defies exact biochemical treatment, and existing techniques, which represent data using equations with time-varying stoichiometry, are difficult to interpret.
Since the complexity values are not dependent on the magnitude of the measured signal, the proposed method is less sensitive to the data sets measured under different data acquisition conditions.
