Lin Jiao – författare

Translation plays a consequential role in how states govern, manage multilingual affairs, and project influence, yet this role is rarely examined through a comparative, state-centred framework. This Element introduces the State Translation Programme (STP) to analyse translation as state-organised action. Comparing China with Japan, Türkiye, the United States, South Korea, Canada, and Poland, it identifies three strategic modes: Architects build national capacity and identity, Influencers project soft power and shape external narratives, and Administrators manage internal coordination and multilingual governance. China stands out in comparative perspective in seeking to combine all three modes, a pattern this Element terms 'sovereign maximalism'. Tracing these governance functions from imperial dynasties to the contemporary People's Republic, the Element offers a framework for comparative analysis across translation studies and political science.

Translation plays a consequential role in how states govern, manage multilingual affairs, and project influence, yet this role is rarely examined through a comparative, state-centred framework. This Element introduces the State Translation Programme (STP) to analyse translation as state-organised action. Comparing China with Japan, Türkiye, the United States, South Korea, Canada, and Poland, it identifies three strategic modes: Architects build national capacity and identity, Influencers project soft power and shape external narratives, and Administrators manage internal coordination and multilingual governance. China stands out in comparative perspective in seeking to combine all three modes, a pattern this Element terms 'sovereign maximalism'. Tracing these governance functions from imperial dynasties to the contemporary People's Republic, the Element offers a framework for comparative analysis across translation studies and political science.

This monograph provides a detailed and systematic introduction to the application of deep learning technology in the intelligent monitoring of crop diseases and pests. Taking 24 types of crop pests, wheat aphids, and wheat diseases with complex backgrounds as examples, a large-scale crop pest and disease dataset was constructed to provide necessary data support for the deep learning module. Various schemes for identifying and detecting large-scale crop diseases and pests based on deep convolutional neural network technology have also been proposed. This book can be used as a reference for teachers and students majoring in agriculture, computer science, artificial intelligence, intelligent science and technology, and other related fields in higher education institutions. It can also be used as a reference book for researchers in fields such as image processing technology, intelligent manufacturing, and high-tech applications.