scholarly journals Chinese Yuan Per Sdr During Covid-19

2021 ◽  
Vol 2 (2) ◽  
Author(s):  
Debesh Bhowmik
Keyword(s):  
Chinese Yuan

scholarly journals Analysis of Cryptocurrency Dynamics in the Emerging Market Economies: Does Reinforcement or Substitution Effect Prevail?

SAGE Open ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 215824402110025
Author(s):  
Chika Anastesia Anisiuba ◽  
Obiamaka P. Egbo ◽  
Felix C. Alio ◽  
Chuka Ifediora ◽  
Ebele C. Igwemeka ◽  
...  
Keyword(s):  
Emerging Market ◽  
Substitution Effect ◽  
Global Market ◽  
Emerging Market Economies ◽  
Vector Error Correction ◽  
Market Economies ◽  
Vector Error ◽  
The Status ◽  
Chinese Yuan ◽  
Digital Assets

We analyzed cryptocurrency dynamics in the global U.S. dollar–denominated market and the emerging market economies (EMEs) with a view to ascertaining whether activities in these markets are predominantly shaped by reinforcement or substitution effect. Cryptocurrencies analyzed include the Bitcoins, Ethereum, Litecoin, Steller, Bitcoin Cash, and USD Tether. The results suggest that, on average, correlation between digital assets in the cryptocurrencies’ ecosystem is positive. However, there is evidence of an outlier with respect to the USD Tether (USDT) in the global market, revealing that the USDT is negatively associated with all other cryptocurrencies. This is supported by the dynamic regression results that provided evidence of reinforcement effect in favor of the USDT in the global crypto market, thus confirming the status of the USDT as “Stablecoin” as it is pegged 1:1 to USD. In the global market context, the results also revealed that USDT/USD returns had identical outliers that could portend lesser chances of extreme gains or losses compared with suggestions of extreme gains or losses in the EMEs. Furthermore, USDT did not seem to have similar evolution in the EMEs where it had relatively marginal influence in the markets. The vector error correction (VEC) estimate showed mixed results between Altcoins in all the markets; moreover, our finding showed that reinforcement effects hold in favor of Steller (XLM) both in the Russian ruble and Indian rupee crypto markets, whereas the Chinese yuan crypto market was predominantly characterized by substitution effect in favor of Bitcoin.

Spatial-Temporal Dynamics of Disease Burden Attributable to Pm2.5 Exposure in China From 2000 to 2016

2021 ◽  
Author(s):  
Ting Cao
Keyword(s):  
Policy Making ◽  
Disease Burden ◽  
Temporal Dynamics ◽  
Comprehensive Evaluation ◽  
Fine Particulate Matter ◽  
Economic Loss ◽  
Northwest China ◽  
Economic Losses ◽  
Air Pollution Control ◽  
Chinese Yuan

Abstract In recent years, long-term exposure to ambient fine particulate matter (PM2.5) has slowly increased both morbidity and mortality for Chinese people, becoming a leading problem for public health efforts. However, spatial-temporal dynamics of disease burden attributable to PM2.5 exposure still lacks a comprehensive evaluation so as to provide inadequate supports for policy making and improvement. Here, we used the exposure-response function to derive the spatial-temporal dynamics of disease burden attributable to PM2.5 pollution in China. We found the fact that economic loss attributable to PM2.5 increased by 93% from 35 billion Chinese Yuan (95% CI: 14-52) to 536 billion Chinese Yuan (95%CI: 236-753) during the period of 16 years. Digging further, we discovered a substantiate level of regional differences, with the disease burden being the most severe in East China and the least severe in the Northwest China. Other than that, there existed a spatial aggregation of health-related economic losses among Chinese cities. Our paper made an evaluation on the spatial-temporal dynamics of health effects attributed to PM2.5, an evaluation that could provide more insights to future policy making of the air pollution control for China and other developing countries.

scholarly journals The State-of-the-Art Technology of Currency Identification

2017 ◽  
Vol 9 (3) ◽  
pp. 58-72 ◽  
Author(s):  
Guangyu Wang ◽  
Xiaotian Wu ◽  
WeiQi Yan
Keyword(s):  
Feature Extraction ◽  
New Zealand ◽  
Comparative Study ◽  
State Of The Art ◽  
The State ◽  
Classification Algorithms ◽  
Security Issue ◽  
The Public ◽  
Us Dollar ◽  
Chinese Yuan

The security issue of currency has attracted awareness from the public. De-spite the development of applying various anti-counterfeit methods on currency notes, cheaters are able to produce illegal copies and circulate them in market without being detected. By reviewing related work in currency security, the focus of this paper is on conducting a comparative study of feature extraction and classification algorithms of currency notes authentication. We extract various computational features from the dataset consisting of US dollar (USD), Chinese Yuan (CNY) and New Zealand Dollar (NZD) and apply the classification algorithms to currency identification. Our contributions are to find and implement various algorithms from the existing literatures and choose the best approaches for use.

scholarly journals How has the Chinese Yuan Evolved since the 2005 Reform?

2019 ◽  
Vol 2 (3) ◽  
Author(s):  
James J. Kung ◽  
◽  
Wen-Ying Lin ◽  
Keyword(s):  
Chinese Yuan

The State-of-the-Art Technology of Currency Identification

2018 ◽  
pp. 252-269
Author(s):  
Guangyu Wang ◽  
Xiaotian Wu ◽  
WeiQi Yan
Keyword(s):  
Feature Extraction ◽  
New Zealand ◽  
Comparative Study ◽  
State Of The Art ◽  
The State ◽  
Classification Algorithms ◽  
Security Issue ◽  
The Public ◽  
Us Dollar ◽  
Chinese Yuan

The security issue of currency has attracted awareness from the public. De-spite the development of applying various anti-counterfeit methods on currency notes, cheaters are able to produce illegal copies and circulate them in market without being detected. By reviewing related work in currency security, the focus of this paper is on conducting a comparative study of feature extraction and classification algorithms of currency notes authentication. We extract various computational features from the dataset consisting of US dollar (USD), Chinese Yuan (CNY) and New Zealand Dollar (NZD) and apply the classification algorithms to currency identification. Our contributions are to find and implement various algorithms from the existing literatures and choose the best approaches for use.

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