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The CLSC network proposed in the paper consists of four echelons in the forward chain and five echelons in the backward chain.
For this purpose, four echelons (suppliers, manufacturer, distribution center and customer) in forward chain and six echelons (collection center, repair center, dismantling center, decomposition center, disposal site and secondary market) in backward chain have been considered.
For the machine learning models, as features we used two sets of bigrams taken from a forward dependency chain which is a dependency path from the root of a dependency tree to the first protein and from a backward chain which is a dependency path from the root to the second protein.
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It operates by backward chaining.
Both forward chaining and backward chaining are used collectively during the inference process.
It is materialised by a backward chaining approach based on argumentation.
This result is unexpected, because classical planning methods produce backward chaining in this task.
On the one hand we extend these classes, on the other hand we introduce decidable classes based on backward chaining.
Both forward chaining and backward chaining are used collectively during the inference process, which is mainly driven by premises and conditions with the highest factors of confidence.
The findings are: (i) forward chaining is significantly more predominant, and backward chaining is significantly less frequent, compared to other possibilities or arrow entering.
Treatment integrity in completing critical steps of discrete-trial and backward chaining procedures were measured using a multiple baseline design across participants, counterbalanced for procedures.
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