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This can be very challenging since high consequence events are dominated by extreme conditions.
DNA sequencing of inverted repeats can be very challenging since snap back to form self dimers can interfere with polymerization.
Dynamical analyses of large networks can be very challenging since the size of the state transition graph increases exponentially with network size.
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Measurement of weak signals is very challenging since the signals may be buried by noise.
Testing complex data structures (e.g., containers) is very challenging since testing software with simple states is already hard.
This deployment is very challenging since embedded platforms often provide limited resources such as computing performance, memory, and power.
In particular, simultaneous realization of double negativity and isotropy, critical in sub-wavelength focusing, is very challenging since anisotropy is usually unavoidable in resonance-based metamaterials.
The study of substrate specificity of protein phosphatases (PPs) is very challenging since it is difficult to prepare a suitable phosphorylated substrate.
Extensive work in the literature so far has shown that this is very challenging, since practically all metals grow 3D, with multi-height islands forming easily.
Due to MUI, timing and carrier synchronization in CDMA is very challenging since compensating TO and CFO for one user may cause synchronization loss to other users.
Computing the evolution dynamics, however, is very challenging since the control and noise Hamiltonians do not commute at different times; sequential application of the resulting time-dependent, non-commuting operations gives rise to both dephasing and depolarization errors, mandating approximation methods.
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