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Traditional edge detection methods typically require uniform grid point distribution.
Current methods typically require 24 h and use target inefficiently.
These methods typically require large quantities of highly purified p38α protein.
Those methods typically require that the nanoparticle be able to recognize a particular cell or disease state.
Mechanistic-empirical pavement design methods typically require the identification of the complex Young's modulus and Poisson's ratio for the characterization of bituminous layers.
Existing gradient coil design methods typically require some predetermined surface to be specified upon which the precise locations of coil windings are optimised with respect to gradient homogeneity and other measures of coil performance.
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This avoids the use of Markov chain Monte Carlo (MCMC) methods typically requiring expert knowledge.
Previous methods typically required 100 mCi Cs-137 sources, but an increase in solid angle between source and sample as well as improved timing resolution have allowed the use of sources with activities less than 10 μCi.
The development of effective multi-modality imaging methods typically requires an efficient information fusion model, particularly when combining structural images with a complementary imaging modality that provides functional information.
Both fractional factorial design and Taguchi's method typically require 2n test conditions, n=2,3,4,…, and hence do not take full advantage of this fact.
The standard finite element method typically requires intensive remeshing to adequately capture the geometry of the cracks and high accuracy is thus often sacrificed in favor of scalability, and vice versa.
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