The transductions studied in this paper have several real-world applications and implications beyond theoretical analysis. For example, the ability to compute string homomorphisms, map-reverse, map-duplicate, and other transductions can be directly applied in data preprocessing tasks in natural language processing pipelines. These transductions can help in data cleaning, normalization, and transformation, leading to improved model performance and accuracy. Additionally, understanding the expressivity of transformers in terms of transductions can guide the development of more efficient and effective transformer architectures for various NLP tasks, contributing to advancements in the field of artificial intelligence and language processing.