stands for Digital Object Identifier
and is the unique identifier for objects on the internet. It can be used to create persistent link and to cite articles.
Using DOI as a persistent link
To create a persistent link, add「http://dx.doi.org/」
before a DOI.
For instance, if the DOI of an article is 10.5297/ser.1201.002 , you can link persistently to the article by entering the following link in your browser: http://dx.doi.org/ 10.5297/ser.1201.002 。
The DOI link will always direct you to the most updated article page no matter how the publisher changes the document's position, avoiding errors when engaging in important research.
Cite a document with DOI
When citing references, you should also cite the DOI if the article has one. If your citation guideline does not include DOIs, you may cite the DOI link.
DOIs allow accurate citations, improve academic contents connections, and allow users to gain better experience across different platforms. Currently, there are more than 70 million DOIs registered for academic contents. If you want to understand more about DOI, please visit airiti DOI Registration （ doi.airiti.com ） 。
洪哲縺 , Masters Advisor：林旭信
繁體中文 DOI： 10.6840/cycu201500920
- Chu, J. L., Kang, H., Tam, C. T., C. K., Chen, C. T., 2008, “Seasonal Forecast for Local Precipitation over Northern Taiwan Using Statistical Downscaling,” Journal of Geophysical Research Atmospheres, Vol. 113(D12118).
- Ekstro ̈m, M., Fowler, H. J., Kilsby, C. G., and Jones, P. D., 2005, “New Estimates of Future Changes in Extreme Rainfall Across the UK Using Regional Climate Model Integrations,” Journal of Hydrology, Vol. 300 , pp.234-251.
- Hanel, M., Mrkvicková, M., Máca, P., Vizina, A., and Pech, P.: Eval-uation of Simple Statistical Downscaling Methods for Monthly Regional Climate Model Simulations with Respect to the Estimated Changes in Runoff in the Czech Republic, Water Resour. Manag, 27, 5261–5279, doi: 10. 1007/s11269-013-0466-1, 2013.
- IPCC, Climate Change 2007: The Physical Scientific Basis. Contribution of Working GroupⅠto the Fourth Assessment Report of the Integergovernmental Panel on Climate Change, 2007.
- Jacob, T., Usman, U. A., Bemdoo, S., Susan, A. A., 2015, “Short-term Electrical Energy Consumption Forecasting Using GMDH-type Neural Network.” Journal of Electrical and Electronic Engineering, Vol. 3(3), pp.42-47.
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