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The accurate prediction of visitor flow in tourist attractions presents a significant challenge within the tourism industry and holds substantial reference value for both park management and tourist experiences. Addressing this, our study develops a predictive model specifically tailored to tourist sites using trajectory data. Recognizing the limitations of current algorithms in identifying accurate stay regions, we utilize a segmentation method predicated on change points. This approach integrates a Back Propagation (BP) neural network with the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering algorithm to enhance the precision of stay region identification. Building upon this foundation, we further incorporate Gaussian fitting techniques to construct a comprehensive crowd prediction model for tourist attractions. The research results verify that the model in this paper can estimate the passenger flow better by predicting the passenger flow of Zhongshan Park in city A. It is found that when the passenger flow is below 15000, the passenger flow is less. When the passenger flow is larger in the range of 15000~30000, and when the passenger flow is more than 30000, it will be saturated and crowded, and the model constructed in this paper has a more accurate passenger flow. The model built in this paper has a high accuracy of people flow prediction value.

This paper investigates the application of exploring spatial clustering techniques in travel recommendation systems to improve recommendation accuracy and user satisfaction. A system that can provide personalized travel information has been designed by analyzing the spatial data and behavioral patterns of travelers. With this approach, the travel experience can be optimized by considering geographic location, user preferences, and tourist attraction characteristics. User data and attraction characteristics are analyzed in this paper to obtain accurate travel recommendations. Combining the traveler’s interest matrix and the attraction area heat algorithm is used to optimize the recommendation process. This recommender system reduces the root mean square error (RMSE) and mean absolute error (MAE) by 0.487% and 21-60%, respectively, compared with the traditional algorithm in the travel recommendation error analysis, and improves the accuracy rate to 96.33%, with a significant increase in the recall rate. The spatial clustering cluster analysis demonstrates that the chosen number of clusters can effectively enhance the clustering quality and maximize the recommendation results. By effectively utilizing spatial data of users and attractions, the system can provide personalized travel recommendations and improve user experience with high accuracy and recall, demonstrating better recommendation performance than traditional methods.

The digital era has ushered in transformative changes for the tourism industry, with virtual reality (VR) technology emerging as a pivotal innovation. This study investigates VR’s role in revolutionizing tourism by offering immersive experiences that enhance the enjoyment of travel and contribute to the preservation and transmission of cultural heritage. Our research assesses VR’s effects on tourism experiences and economic performance by employing panoramic roaming and multi-projection immersive environments. Findings reveal a dramatic increase in tourism returns, with a notable 80% rise in total return on assets for CITS, from 10% in 2013 to 18% in 2023. Additionally, VR has significantly influenced tourists’ spatial-temporal behaviors, boosting their engagement and interest in heritage sites. This paper highlights VR technology’s potential to augment tourism experiences and economic gains, underscoring its importance in the sector’s digital evolution.

During the Shang and Zhou Dynasties, China was distinctive for its use of leaded tin bronze, which diverged from bronzes in the Eurasian steppe. A reanalysis of published chemical data of late Shang and Western Zhou bronze vessels aimed to shed light on the purposeful addition of tin and lead. The findings demonstrate that the concentration of tin follows two separate patterns, with the predominant one stabilizing at about 15% and the secondary one spreading below 7%, reflecting diverse metallurgical practices. Furthermore, the linear variation between copper and lead (Cu-Pb) implies that lead was added to substitute for copper, probably owing to resource scarcity or copper shortages in the growing bronze industry of early states.

Scientific papers are an important form for researchers to summarize and display their research results. Information mining and analysis of scientific papers can help to form a comprehensive understanding of the subject. Aiming at the ignorance of contextual semantic information in current topic mining and the uncertainty of screening rules in association evolution research, this paper proposes a topic mining evolution model based on the BERT-LDA model. First, the model combines the contextual semantic information learned by the BERT model with the word vectors of the LDA model to mine deep semantic topics. Then construct topic filtering rules to eliminate invalid associations between topics. Finally, the relationship between themes is analyzed through the theme evolution, and the complex relationship between the themes such as fusion, diffusion, emergence, and disappearance is displayed. The experimental results show that, compared with the traditional LDA model, the topic mining evolution model based on BERTLDA can accurately mine topics with deep semantics and effectively analyze the development trend of scientific and technological paper topics.

To understand the firing technology and the level of production of glazed tiles in the Ming Dynasty, an ultraviolet-visible-near-infrared (UV-Vis-NIR) spectrophotometer and an energy-dispersive X-ray fluorescence (XRF) spectrometer were used to systematically analyze the glaze color and chemical composition of glazed tiles unearthed from the southern area of the Bao'ensi site. The results revealed that little difference exists in the main wavelengths of the same glazed tile samples, but that the colored elements of glaze with different colors are different. Iron (Fe) is the main element that affects the color of yellow glaze; copper (Cu) is the main element that affects the color of green glaze; and the main colorants that affect the color of black glaze are Fe, Cu, and manganese (Mn). The yellow and black glaze unearthed from the southern area of the Bao'ensi site were relatively mature in terms of raw material composition and technological formula, whereas the raw material and technological formula of green glaze were not stable enough. To meet temple construction needs, workers were skilled in firing glazed tiles with different glaze color requirements, indicating that the glaze color of the glazed tiles was relatively mature at that time., Architectural glazed tile, a type of low-temperature lead-glazed pottery, is a common building material used in traditional Chinese architecture with good decorative and waterproof functions. The process of fabricating glass [...]

Inductively coupled plasma (ICP) is a well-known source for atomic spectroscopy, either using optical emission spectroscopy (OES) or mass spectrometry (MS) as a detection technique. The high energy and temperatures [...]