Yi Zhang

May 17, 2024 | nature.com | Yunhao Zheng |Yi Zhang |Yu Chi Liu

Tourist movement patterns among attractions are complex and variable, and understanding such patterns can help manage tourist destinations more effectively. However, previous studies on tourist movement utilising complex networks have not explored the network motif approach comprehensively. Therefore, we adopted a network motif approach using social media data to extract and analyse motifs in a city network. This study analyses the attractions corresponding to the nodes in each motif, revealing the connection patterns between these attractions. We also discuss motifs between attractions with different types and titles. Popular attractions play a significant role in a local network while other attractions serve distinct functions within the network. This study’s findings enhance the significance of network motifs in examining tourist movement and deepen the understanding of recurring movement patterns between attractions. Moreover, they assist managers in developing policy tools for intelligent tourism destination marketing and planning that cater to tourists’ needs.

Feb 3, 2024 | nature.com | Yueming Sun |Wen Peng |Junwei Tang |Sheng Zhang |Yanhong Gu |Yi Zhang | +4 more

Colorectal cancer (CRC) is a global health challenge, and patient education plays a crucial role in its early detection and treatment. Despite progress in AI technology, as exemplified by transformer-like models such as ChatGPT, there remains a lack of in-depth understanding of their efficacy for medical purposes. We aimed to assess the proficiency of ChatGPT in the field of popular science, specifically in answering questions related to CRC diagnosis and treatment, using the book “Colorectal Cancer: Your Questions Answered” as a reference. In general, 131 valid questions from the book were manually input into ChatGPT. Responses were evaluated by clinical physicians in the relevant fields based on comprehensiveness and accuracy of information, and scores were standardized for comparison. Not surprisingly, ChatGPT showed high reproducibility in its responses, with high uniformity in comprehensiveness, accuracy, and final scores. However, the mean scores of ChatGPT’s responses were significantly lower than the benchmarks, indicating it has not reached an expert level of competence in CRC. While it could provide accurate information, it lacked in comprehensiveness. Notably, ChatGPT performed well in domains of radiation therapy, interventional therapy, stoma care, venous care, and pain control, almost rivaling the benchmarks, but fell short in basic information, surgery, and internal medicine domains. While ChatGPT demonstrated promise in specific domains, its general efficiency in providing CRC information falls short of expert standards, indicating the need for further advancements and improvements in AI technology for patient education in healthcare.