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The simulation domain is obtained rotating the 2D shape in Figure 3. Two water-like dielectric regions (ϵ W = 80) are connected via a cylindrical pore (6.5 Å radius) embedded in a membrane slab (ϵ M = 6) 30 Å wide.
Illustrative numerical example of 2D shape optimization is presented.
An algorithm for fairing two-dimensional (2D) shape formed by digitised data points is described.
The efficiency and accuracy of the proposed approach are demonstrated through four 2D shape optimization examples.
Although symbolic understanding has long been studied, little is known about the 2D shape information children use to relate symbols to their 3D referents.
Moment invariants have been thoroughly studied and repeatedly proposed as one of the most powerful tools for 2D shape identification.
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With the exception of a single maximum in post-ITG (LO), no 2D shape-sensitive site is significantly involved in 3D SfS.
The question then arises as to whether the reverse also holds: are all 2D shape-sensitive regions and particularly LOC, also involved in extracting 3D shape?
A number of 2D shape-sensitive sites in post-ITG (LO) and LOS reach significance in the conjunction defining 3D SfT sensitivity, but none in the mid-FG (LOa) region (Table 5).
The results of Kleinman and Brodzinsky were similar to those of Thompson et al.: the younger participants' 2D shape-matching performance was 38.6percentt higher than that of the older participants.
Both the group and the single-subject analyses revealed that the regions involved in extracting 3D SfT and 3D SfS were 2D shape-sensitive (Figs 6 8, Table 2).
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