In that sense, this paper contributes to a better understanding of the historical and contemporary dynamics in European LCCs choice of airports and, in particular, the long-term effects that this disruptive business model have had for airports. This is increasingly important in the context of a potential recovery path from the effects of the response to the Covid-19 pandemic. This paper also attempts to settle academic discussions that attach LCC development to secondary/regional airports disregarding the wide range of strategies used by airlines and airports.
The devastating spread of the novel coronavirus, named COVID-19, starting its journey from Wuhan Province of China on January 21st, 2020, has now threatened lives of almost all the countries of the world in different magnitudes. Mostly the developed countries have been hit hard, besides the emerging countries like China, India and Brazil. The scientists and the policy makers are in dark with respect to its spread and claiming lives in coming days.

The present study aims to forecast the number of incidences in severely affected seven countries, USA, UK, Italy, Spain, France, China and India, for the period July 12-Septmeber 11, 2020 and compares the forecasted values with the actual values to judge its depth of severity and growth.

The study uses Box-Jenkins method of forecasting in an Autoregressive Integrated Moving Average (ARIMA) structure on the basis of the daily data published by World Health Organization from January 21st to July 11, 2020.

It is observed that USA and India are the two countriesforecasted period will be diminishing. The mean difference test results between the forecasted and actual values in level and growth forms show that in the former case, USA, India, UK will face increasing forecast than the actual number but in the latter case, all of the countries will face significantly decreasing growth rates in the forecasted values compared to their actual growth values.The development of novel digital auscultation techniques has become highly significant in the context of the outburst of the pandemic COVID 19. The present work reports the spectral, nonlinear time series, fractal, and complexity analysis of vesicular (VB) and bronchial (BB) breath signals. The analysis is carried out with 37 breath sound signals. https://www.selleckchem.com/products/etc-159.html The spectral analysis brings out the signatures of VB and BB through the power spectral density plot and wavelet scalogram. The dynamics of airflow through the respiratory tract during VB and BB are investigated using the nonlinear time series and complexity analyses in terms of the phase portrait, fractal dimension, Hurst exponent, and sample entropy. The higher degree of chaoticity in BB relative to VB is unwrapped through the maximal Lyapunov exponent. The principal component analysis helps in classifying VB and BB sound signals through the feature extraction from the power spectral density data. The method proposed in the present work is simple, cost-effective, and sensitive, with a far-reaching potential of addressing and diagnosing the current issue of COVID 19 through lung auscultation.We analytically study the SEIR (Susceptible Exposed Infectious Removed) epidemic model. The aim is to provide simple analytical expressions for the peak and asymptotic values and their characteristic times of the populations affected by the COVID-19 pandemic.We describe in this paper a hybrid intelligent approach for forecasting COVID-19 time series combining fractal theory and fuzzy logic. The mathematical concept of the fractal dimension is used to measure the complexity of the dynamics in the time series of the countries in the world. Fuzzy Logic is used to represent the uncertainty in the process of making a forecast. The hybrid approach consists on a fuzzy model formed by a set of fuzzy rules that use as input values the linear and nonlinear fractal dimensions of the time series and as outputs the forecast for the countries based on the COVID-19 time series of confirmed cases and deaths. The main contribution is the proposed hybrid approach combining the fractal dimension and fuzzy logic for enabling an efficient and accurate forecasting of COVID-19 time series. Publicly available data sets of 10 countries in the world have been used to build the fuzzy model with time series in a fixed period. After that, other periods of time were used to verify the effectiveness of the proposed approach for the forecasted values of the 10 countries. Forecasting windows of 10 and 30 days ahead were used to test the proposed approach. Forecasting average accuracy is 98%, which can be considered good considering the complexity of the COVID problem. The proposed approach can help people in charge of decision making to fight the pandemic can use the information of a short window to decide immediate actions and also the longer window (like 30 days) can be beneficial in long term decisions.The generalized logistic equation is used to interpret the COVID-19 epidemic data in several countries Austria, Switzerland, the Netherlands, Italy, Turkey and South Korea. The model coefficients are calculated the growth rate and the expected number of infected people, as well as the exponent indexes in the generalized logistic equation. It is shown that the dependence of the number of the infected people on time is well described on average by the logistic curve (within the framework of a simple or generalized logistic equation) with a determination coefficient exceeding 0.8. At the same time, the dependence of the number of the infected people per day on time has a very uneven character and can be described very roughly by the logistic curve. To describe it, it is necessary to take into account the dependence of the model coefficients on time or on the total number of cases. Variations, for example, of the growth rate can reach 60%. The variability spectra of the coefficients have characteristic peaks at periods of several days, which corresponds to the observed serial intervals. The use of the stochastic logistic equation is proposed to estimate the number of probable peaks in the coronavirus incidence.