In this paper a core reloading technique using Artificial Bee Colony algorithm, ABC, is presented in the context of finding an optimal configuration of fuel assemblies.
Traditionally, assembly planning and scheduling problems are often associated with finding assembly sequence and assembly resource location (Nof 1997).
In the described procedure, the building assemblies are selected by finding a trade-off between a set of designer-specified criteria.
In our work, we use likelihood of a genome assembly as an optimization criterion, with the goal of finding the assembly with the highest likelihood.
This finding points to an assembly mechanism in which lectin domains may remain bound to receptors on the cell surface throughout assembly of the cytolytic toxin core and explains the hemagglutinating activity of purified toxin.
This was too long for finding the best assembly protocol.
Despite this intense focus on finding the exact assembly that is the real 'neurotoxin', there is little, if any, consensus in the field regarding this issue, in large measure because it has been difficult to unequivocally demonstrate that some of the proposed misfolded neurotoxins exist in vivo.
The definition of a k-assembly and a cell assembly served as motivation to solve the problem of finding all cell assemblies and tight sets that generate them, in particular minimal k-cores.
However, due to the complexity of the definition; the number of neurons on a brain-sized neuronal network; and the number of connections per neuron, it still may not be possible, in practice, to solve the problem of finding all cell assemblies.
2. NG50 contig length: a measure of average contig length (higher = better) 3. Amount of scaffold sequence gene-sized scaffolds (≥25 Kbp): measured as the absolute difference from expected genome size, this helps describe the suitability of an assembly for gene finding purposes (lower = better).
