The difficulty of Computational Wind Engineering (hereafter CWE) is described from the viewpoints of Computational Fluid Dynamics (hereafter CFD) technique.
First, the difficulty of computational prediction of subcellular localization is well recognized (Lei and Dai 2005; Chou and Shen 2007).
Because of the difficulty of computational searches for sRNA binding sites [ 42], we restricted this search to pXO1 (NC_007322), which is where the putative sRNA genes were located.
Also, the molecule (in addition to the protein) undergoes conformational changes, underscoring the difficulty of computational methods to model such interactions.
The current difficulties of developing computational methods for the prediction of miRNA-disease associations lie in three aspects.
Even considering the inherent difficulties of the computational prediction of miRNA targets, which often results large number of false positive targets [ 32], it is possible that our conservative approach has underestimated the number of candidate targets.
Because these DNAP mechanisms are considered to be potentially simpler than multisubunit RNAP mechanisms, this elevates the degree of difficulty for computational analysis of multisubunit RNAPs.
When compared to the implementation of SLAM in 2D environments, airborne implementation imposes several difficulties in terms of computational complexity and loop closure, with high nonlinearity in both vehicle dynamics and observations.
According to Deutsch, the insight for that paper came from a conversation in the early eighties with the physicist Charles Bennett, of I.B.M., about computational-complexity theory, at the time a sexy new field that investigated the difficulty of a computational task.
To overcome such difficulty in terms of computational complexity, several approximations have been designed, such as imposing a previous ordering on the domain attributes that restrict the number of Bayesian structures to be learned or using other approaches trying to reduce the state space of this problem.
