1998
DOI: 10.1016/s0020-7683(97)89372-9
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The equilibrium of the convergence point in two-strand yarn plying

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Cited by 29 publications
(16 citation statements)
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“…In such cases, being applied to the both tails of a linear DNA hairpin (or to the opposite strands of a cyclic DNA duplex) confined within the nanopore, the torques with respect to the relevant Manning clouds ought to promote some specific writhing -and thus refolding -of the DNA tertiary structure, to form a combination of balanced plies with end loops. [59][60][61] It is intuitively clear that, in turn, such compact tertiary superhelical structuresought to be effectively translocated through the nanopore at times much shorter -and will cause much deeper ionic current dips than those of the corresponding completely unfolded DNA tertiary structures, just as observed in experiments. 4 In contrast, linear DNA hairpins folded only partially (or cyclic DNA duplexes), that is, without plies, could in many cases clog the nanopore or even be rejected by the latter -just as revealed by computer simulations.…”
mentioning
confidence: 87%
“…In such cases, being applied to the both tails of a linear DNA hairpin (or to the opposite strands of a cyclic DNA duplex) confined within the nanopore, the torques with respect to the relevant Manning clouds ought to promote some specific writhing -and thus refolding -of the DNA tertiary structure, to form a combination of balanced plies with end loops. [59][60][61] It is intuitively clear that, in turn, such compact tertiary superhelical structuresought to be effectively translocated through the nanopore at times much shorter -and will cause much deeper ionic current dips than those of the corresponding completely unfolded DNA tertiary structures, just as observed in experiments. 4 In contrast, linear DNA hairpins folded only partially (or cyclic DNA duplexes), that is, without plies, could in many cases clog the nanopore or even be rejected by the latter -just as revealed by computer simulations.…”
mentioning
confidence: 87%
“…Software modelled on this principle can be "trained" to capture non-linear relationship between the input and the output variables. A number of different principles/models of ANN have been developed and the present most commonly used model is the layered feed forward neural network with multiple layers and with the back propagation-learning algorithm [16]. An ANN is a computing system composed of a number of highly interconnected layers of simple neurons like processing elements which process information by a dynamic response to external inputs.…”
Section: Artificial Neural Network (Ann)mentioning
confidence: 99%
“…changes the force and moment equilibrium equations for the idealised n-ply structure are given by Equations (34) and (35) of Fraser and Stump (1998a):…”
Section: R(s)mentioning
confidence: 99%