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Supercritical Fluid Manufacture
Published in Anthony J. Hickey, Sandro R.P. da Rocha, Pharmaceutical Inhalation Aerosol Technology, 2019
Ana Aguiar-Ricardo, Eunice Costa
The González equation was modified using the q-exponential function and the variables and the adjustable parameters with the meaning as defined above (Tabernero et al. 2014): () yieq=ρSCFk.mγ.expq(AqT)
Cause-specific investigation of primary delays of Wuhan–Guangzhou HSR
Published in Transportation Letters, 2020
Chao Wen, Zhongcan Li, Ping Huang, Javad Lessan, Liping Fu, Chaozhe Jiang
Some studies have made contributions on distributions of delay and the respective fitness models. The Weibull distribution, Gamma distribution, and Log-normal distributions have been adopted in several studies (Yuan, Goverde, and Hansen 2002; Higgins and Kozan 1998). It shows that the distributional form of primary delays, and the affected number of trains can be well approximated by classical methods such as Log-normal distribution and line regression models (Wen et al. 2017). A q-exponential function is used to demonstrate the distribution of train delays on the British railway network (Briggs and Beck 2007). Using spatial and temporal resolution transport data from the UK road and rail networks, and the intense storms of June 28 2012 as a case study, a novel exploration of the impacts of an extreme event has been carried out in (Jaroszweski et al. 2015). Given the HSR operation data, maximum-likelihood estimation was used to determine the probability distribution of the different disruption source, and the distributions of affected trains; however, the models of primary delay consequences have not been established in detail (Xu, Corman, and Peng 2016). Probabilistic distribution functions of both train arrival and departure delays at the individual station were derived in general based on the data from Beijing–Shanghai HSR (Guo et al. 2015). Van der Meer et al. mined peak hours, rolling stock, weather data and developed a predictive model involving the mining of track occupation data for delays estimations (Milinkovic et al. 2013).