The earliest paper currencies in existence in China were handcrafted during the Yuan dynasty.These currencies were scientifically excavated from different ruins or tombs,whereas scientific analyses of the papers are r...The earliest paper currencies in existence in China were handcrafted during the Yuan dynasty.These currencies were scientifically excavated from different ruins or tombs,whereas scientific analyses of the papers are rare.This study used optical and scanning electron microscopy to examine the fibers collected in Yuan dynasty paper currencies in conjunction with the Herzberg staining method.Despite differences in circulation period,paper fibers in both Zhi Yuan Tong Xing Bao Chao(two Guan)and Zhong Tong Yuan Bao Jiao Chao(one Guan and 500 Wen,issued in Zhi Zheng period)were identified as similar papermaking materials,bast fibers of mulberry bark.The results indicate that mulberry bark,a durable papermaking material used since ancient times,was mainly utilized as a raw material in these Yuan dynasty paper currency.This fiber identification work solved the critical problem of papermaking material in the Yuan dynasty paper currency and provided important information for conserving these precious cultural relics.展开更多
With the gradual application of central bank digital currency(CBDC)in China,it brings new payment methods,but also potentially derives new money laundering paths.Two typical application scenarios of CBDC are considere...With the gradual application of central bank digital currency(CBDC)in China,it brings new payment methods,but also potentially derives new money laundering paths.Two typical application scenarios of CBDC are considered,namely the anonymous transaction scenario and real-name transaction scenario.First,starting from the interaction network of transactional groups,the degree distribution,density,and modularity of normal and money laundering transactions in two transaction scenarios are compared and analyzed,so as to clarify the characteristics and paths of money laundering transactions.Then,according to the two typical application scenarios,different transaction datasets are selected,and different models are used to train the models on the recognition of money laundering behaviors in the two datasets.Among them,in the anonymous transaction scenario,the graph convolutional neural network is used to identify the spatial structure,the recurrent neural network is fused to obtain the dynamic pattern,and the model ChebNet-GRU is constructed.The constructed ChebNet-GRU model has the best effect in the recognition of money laundering behavior,with a precision of 94.3%,a recall of 59.5%,an F1 score of 72.9%,and a microaverage F1 score of 97.1%.While in the real-name transaction scenario,the traditional machine learning method is far better than the deep learning method,and the micro-average F1 score of the random forest and XGBoost models both reach 99.9%,which can effectively identify money laundering in currency transactions.展开更多
文摘The earliest paper currencies in existence in China were handcrafted during the Yuan dynasty.These currencies were scientifically excavated from different ruins or tombs,whereas scientific analyses of the papers are rare.This study used optical and scanning electron microscopy to examine the fibers collected in Yuan dynasty paper currencies in conjunction with the Herzberg staining method.Despite differences in circulation period,paper fibers in both Zhi Yuan Tong Xing Bao Chao(two Guan)and Zhong Tong Yuan Bao Jiao Chao(one Guan and 500 Wen,issued in Zhi Zheng period)were identified as similar papermaking materials,bast fibers of mulberry bark.The results indicate that mulberry bark,a durable papermaking material used since ancient times,was mainly utilized as a raw material in these Yuan dynasty paper currency.This fiber identification work solved the critical problem of papermaking material in the Yuan dynasty paper currency and provided important information for conserving these precious cultural relics.
基金supported by the National Science Foundation of China(No.61602536)the Emerging Interdisciplinary Project of Central University of Finance and Economics(CUFE),and Financial Sustainable Development Research Team.
文摘With the gradual application of central bank digital currency(CBDC)in China,it brings new payment methods,but also potentially derives new money laundering paths.Two typical application scenarios of CBDC are considered,namely the anonymous transaction scenario and real-name transaction scenario.First,starting from the interaction network of transactional groups,the degree distribution,density,and modularity of normal and money laundering transactions in two transaction scenarios are compared and analyzed,so as to clarify the characteristics and paths of money laundering transactions.Then,according to the two typical application scenarios,different transaction datasets are selected,and different models are used to train the models on the recognition of money laundering behaviors in the two datasets.Among them,in the anonymous transaction scenario,the graph convolutional neural network is used to identify the spatial structure,the recurrent neural network is fused to obtain the dynamic pattern,and the model ChebNet-GRU is constructed.The constructed ChebNet-GRU model has the best effect in the recognition of money laundering behavior,with a precision of 94.3%,a recall of 59.5%,an F1 score of 72.9%,and a microaverage F1 score of 97.1%.While in the real-name transaction scenario,the traditional machine learning method is far better than the deep learning method,and the micro-average F1 score of the random forest and XGBoost models both reach 99.9%,which can effectively identify money laundering in currency transactions.
