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Although the direct Monte Carlo simulation (MCS) method can estimate the seismic failure probability accurately, it is inefficient when small failure probabilities are of interest, and especially so if finite element transient analysis is required.
However, it is inefficient when dealing with lossy channels such as vehicular environments.
While this method allows the curator to directly review and verify the changes entered into the database, it is inefficient when performing large numbers of updates.
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On the contrary, biological treatment is inefficient when it comes to the treatment of stabilized leachate due to the narrow range of biodegradable components in leachate, the presence of substances toxic for the microorganism and the limited availability of necessary nutrients for microbial growth (Li et al. 1999).
However, this learning strategy in the canonical PSO is inefficient when complex problems are being optimized.
The sum-of-disjoint-products method used by these algorithms is inefficient when dealing with large Boolean functions.
Most sample preparation methods characteristically involve intensive and repetitive labor, which is inefficient when preparing large numbers of samples from population-scale studies.
The results show that the dominant micro-architecture mismatches to demands of wireless communication protocols, which makes dominant general multi-core processor is inefficient when used in this application field.
The second is that a brute-force ANOVA approach is inefficient when there are many variables.
Prediction about next transition is inefficient when workload is not determined in advance.
By contrast, an observation is inefficient when θ > 1, λ i = 0, and λ j ≠ 0.
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