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In this paper, we propose a novel merge-refactoring approach for automating this task.
This article argues in favor of a new type of Merge, Parallel Merge, which combines the properties of External Merge and Internal Merge.
We demonstrate that our merge algorithm is more robust than prior work.
The authors propose in this paper a more efficient data structure for the merge operations.
In addition, due to the use of the recent development of magnetically tunable assembly techniques, the processing time of this photonic wordpad is greatly reduced compared to using conventional assembly methods, which has an important significance in the large-scale industrial production in the future.
In most respects, Merge is similar to other models of word and phoneme recognition.
The best performance is the proposed combined model with 94% accuracy for non-merge behavior and 78% accuracy for merge behavior, demonstrating the effectiveness of the proposed method and superior performance compared to other methods.

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