Yifei Li

The development of the market poses a threat to the survival of Su embroidery, which highlights the urgency and complexity of protecting intangible cultural heritage in the context of the globalization and emphasizes the need for innovative response strategies. Based on an eight-year longitudinal ethnographic survey conducted in Suzhou from 2017 to 2024, this study delves into how digital technology affects the attitudes of Su embroidery inheritors and analyzes the impact of digital technology on the protection of Su embroidery art. Through the investigations and interviews, this study explores a new paradigm of digital communication that technologies can play a crucial role in preserving and promoting cultural heritage by adapting to the dynamic transmission characteristics of culture authenticity. The research findings provide new insights for empowering cultural value innovation with technologies, suggesting that digitalization can indeed serve as a powerful tool for the protection and revitalization of intangible cultural heritage.

As digital technology evolves rapidly, smart tourism has become a significant trend in the modernization of the industry, relying on advanced tools like big data and cloud computing to improve travelers’ experiences. Despite the growing use of human–computer interaction in museums, there remains a lack of in-depth academic investigation into its impact on visitors’ behavioral intentions regarding museum engagement. This paper employs Cognitive Appraisal Theory, considers human–computer interaction experience as the independent variable, and introduces destination image and satisfaction as mediators to examine their impact on destination loyalty. Based on a survey of 537 participants, the research shows that human–computer interaction experience has a significant positive impact on destination image, satisfaction, and loyalty. Destination image and satisfaction play a partial and sequential mediating role in this relationship. This paper explores the influence mechanism of human–computer interaction experience on destination loyalty and proposes practical interactive solutions for museums, aiming to offer insights for smart tourism research and practice.

Since the concept of artificial intelligence was introduced in 1956, AI technology has been gradually applied in various fields, including journalism. This paper focuses on research related to AI news anchors, and two correlated experiments are applied to examine audiences’ perceived attractiveness of AI news anchors from a psychological dimension. Study 1 focuses on the different variables that influence the behavioral willingness of AI news anchor viewers, while Study 2 focuses on the mediating and moderating variables that influence audiences’ psychological changes. The results indicate that non-humanoid female AI news anchors who use anthropomorphic voices to broadcast news obtain the highest perceived attractiveness among audiences. Additionally, the mediating effect of perceived attractiveness and the negative moderating effect on the inherent impression of traditional news anchors are both verified in the study. Based on the research findings, the implications and suggestions are addressed accordingly.

Human bones have different characteristics in different development stages, so the estimation of bone age can reflect the growth and development level of individuals relatively accurately. Bone age estimation aims to predict the biological age of children, which plays an important role on the diagnosis of some pediatric endocrine diseases. Tradition methods are carried out by doctors, and it is not effective in accuracy and speed. To this end, we proposed a deep-learning based method for bone age estimation. Based on the training set of more than 10000 X-ray images of hand bones from Radiological Society of North America (RSNA), this paper studies the processing, segmentation, feature extraction of X-ray hand bone images by using computer image processing and artificial intelligence learning methods, and uses convolution neural network to process samples and analyze them automatically. The main research work and achievements are as follows: (1) Pre-processing of X-ray hand bone image, unifying the size and cutting, reducing the image area without hand bone; (2) The gray-scale image is transformed into a three-channel image, and pre-processing by EfficientNet of ImageNet. Then convolution neural network is used to learn the features of X-ray hand bone image and evaluate it automatically. Finally, the network is evaluated by the minimum mean square error, so that the minimum mean square error is as close as possible to the minimum value. Through the neural network, the bone age from X-ray hand bone image can be quickly judged, and then it can be applied to clinical research.