An Exploratory Data Analysis of Mazu Culture Research Based on Diaolong-Full-Text Database of Ancient Chinese and Japanese Books

Mazu is the most famous goddess of canal transport in China,and one of the three folk beliefs in China.Japan is our neighbor across the sea.As early as 1000 years ago,Japan was influenced by the Mazu ceremonial culture.Through big data analysis,this study conducted database counting,screening,and analysis on the Mazu culture in Diaolong,the full-text database of Chinese and Japanese ancient books.Besides,it explored the hot topics of concern and emotional attitudes,and then analyzed the important role of Mazu culture in the cultural exchange and mutual learning between China and Japan in the new era,with a view to completing the contemporary task of“people-to-people bond”and achieving common development.

Citing behavior of Chinese books based on citation content

The purpose is to analyze the citing behaviors over books from the perspective of citation content,and to overcome the traditional deficiencies of book impact evaluation based on citation frequencies and book reviews,so as to improve the accuracy and scientificity of book impact evaluation.We collected Chinese books from five disciplines including:computer science,law,medicine,literature and sport science from Amazon.cn.Then we extracted citation contents about these Chinese books from each citing literature manually and built a corpus with 2,288 citation contents.Finally,we analyzed citation behaviors over these Chinese books by mining citation locations,citation intensities,citation lengths and citation sentiments.The experimental results showed that:1)when citing Chinese books,authors from five disciplines had different preferences on citation locations;2)citation intensities mainly ranged from 1 to 3.In addition,citations in literature had more high citation intensities;3)the citation lengths were concentrated between 20 and 160;4)regarding citation sentiments of Chinese books,more than 80%citations were neutral.Compared with negative citations,there were more positive ones.

Seat inventory control methods for Chinese passenger railways

Railway seat inventory control strategies play a crucial role in the growth of profit and train load factor. The railway passenger seat inventory control problem in China was addressed. Chinese passenger railway operation features and seat inventory control practice were analyzed firstly. A dynamic demand forecasting method was introduced to forecast the coming demand in a ticket booking period. By clustering, passengers' historical ticket bookings were used to forecast the demand to come in a ticket booking period with least squares support vector machine. Three seat inventory control methods: non-nested booking limits, nested booking limits and bid-price control, were modeled under a single-fare class. Different seat inventory control methods were compared with the same demand based on ticket booking data of Train T15 from Beijing West to Guangzhou. The result shows that the dynamic non-nested booking limits control method performs the best, which gives railway operators evidence to adjust the remaining capacity in a ticket booking period.

Intelligent Prescription-Generating Models of Traditional Chinese Medicine Based on Deep Learning

Objective:This study aimed to construct an intelligent prescription-generating(IPG)model based on deep-learning natural language processing(NLP)technology for multiple prescriptions in Chinese medicine.Materials and Methods:We selected the Treatise on Febrile Diseases and the Synopsis of Golden Chamber as basic datasets with EDA data augmentation,and the Yellow Emperor’s Canon of Internal Medicine,the Classic of the Miraculous Pivot,and the Classic on Medical Problems as supplementary datasets for fine-tuning.We selected the word-embedding model based on the Imperial Collection of Four,the bidirectional encoder representations from transformers(BERT)model based on the Chinese Wikipedia,and the robustly optimized BERT approach(RoBERTa)model based on the Chinese Wikipedia and a general database.In addition,the BERT model was fine-tuned using the supplementary datasets to generate a Traditional Chinese Medicine-BERT model.Multiple IPG models were constructed based on the pretraining strategy and experiments were performed.Metrics of precision,recall,and F1-score were used to assess the model performance.Based on the trained models,we extracted and visualized the semantic features of some typical texts from treatise on febrile diseases and investigated the patterns.Results:Among all the trained models,the RoBERTa-large model performed the best,with a test set precision of 92.22%,recall of 86.71%,and F1-score of 89.38%and 10-fold cross-validation precision of 94.5%±2.5%,recall of 90.47%±4.1%,and F1-score of 92.38%±2.8%.The semantic feature extraction results based on this model showed that the model was intelligently stratified based on different meanings such that the within-layer’s patterns showed the associations of symptom–symptoms,disease–symptoms,and symptom–punctuations,while the between-layer’s patterns showed a progressive or dynamic symptom and disease transformation.Conclusions:Deep-learning-based NLP technology significantly improves the performance of IPG model.In addition,NLP-based semantic feature extraction may be vital to further investigate the ancient Chinese medicine texts.

Book Review on the Illustrated Seeds of Chinese Medicinal Plants

Medicinal plants are important source for Oriental and Western medicines.There are more than 500 herbs commonly used today in China,in which near 30% of them are seed medicines and over