Proton transfer in bioenergetic proteins

Maróti, Ágnes

doi:10.14232/phd.2578

Cited by 1 publication

(1 citation statement)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…. , d k [9,12] k ≥ 4 k = 2, 3 for congruence join-semidistributivity [k = 2: ∼ 2 3 -minority term] k-ary near unanimity term [10,1] k ≥ 4 k = 3 [majority term] k-cube term (arity: 2 k − 1) [4] k ≥ 3 k = 2 [∼Maltsev term] k-edge term (arity: k + 1) [4] k ≥ 3 k = 2 [∼Maltsev term] (m, n)-parallelogram term m, n ≥ 1 -(arity: m + n + 3) [15] k-ary weak near unanimity term [18] k ≥ 4 k = 3 k-ary cyclic term [2] k ≥ 4 k = 2, 3 Table 3. Idemprimality for random finite models of some familiar strong idempotent linear Maltsev conditions (with parameters)…”

Section: Further Examples Inmentioning

confidence: 99%

Random models of idempotent linear Maltsev conditions. I. Idemprimality

Bergman

Szendrei

2020

Algebra Univers.

View full text Add to dashboard Cite

We extend a well-known theorem of Murskiǐ to the probability space of finite models of a system M of identities of a strong idempotent linear Maltsev condition. We characterize the models of M in a way that can be easily turned into an algorithm for producing random finite models of M, and we prove that under mild restrictions on M, a random finite model of M is almost surely idemprimal. This implies that even if such an M is distinguishable from another idempotent linear Maltsev condition by a finite model A of M, a random search for a finite model A of M with this property will almost surely fail.

show abstract