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The functional and in silico characterization of these elements allowed us to reach several novel conclusions.
Looking in detail at the repeated sequences "lITR-lICR-rICR-rITR-rE" in these elements allowed us to reveal a rather more complex structure.
Detailed analysis of genomic sequences flanking these elements allowed the identification of 33 chimeric retroelements flanked by 10 20 bp long direct repeats.
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These elements allow the application of nearly arbitrary constitutive laws for describing the dynamic joint behavior.
The simultaneous optimization of these elements allows the evaluation of several compromises among design and process variables.
A natural sub-triangulation of these elements allows to reuse the simple distribution schemes previously developed for linear P1 triangles.
The use of these elements allows for the design and fabrication of high-density 3-dimensional circuits that can achieve the interconnect density of biological neural systems.
Although, the insertion of these elements allow to design small antennas their gain decrease depending on the CPW structures are connected.
Unpacking the interactive and reciprocal relationships between these elements allows us to better understand how particular PD programs work and what makes them more or less effective with respect to their learning goals.
In fact, these elements allow to define the operator P m v = ∑ k = 1 m ( v k, v ) v k, which is the orthogonal projection of H and V in V m : = span [ v 1, …, v m ] with their respective norms.
Stand-off annotations can be embedded into these elements, allowing for the inclusion of information such as text-bound locations and n-ary relations between annotations.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com