RESUMO
Flight demand forecasting is a particularly critical component for airline revenue management because of the direct influence on the booking limits that determine airline profits. The traditional flight demand forecasting models generally only take day of the week (DOW) and the current data collection point (DCP) adds up bookings as the model input and uses linear regression, exponential smoothing, pick-up as well as other models to predict the final bookings of flights. These models can be regarded as time series flight demand forecasting models based on the interval between the current date and departure date. They fail to consider the early bookings change features in the specific flight pre-sale period, and have weak generalization ability, at last, they will lead to poor adaptability to the random changes of flight bookings. The support vector regression (SVR) model, which is derived from machine learning, has strong adaptability to nonlinear random changes of data and can adaptively learn the random disturbances of flight bookings. In this paper, flight bookings are automatically divided into peak, medium, and off (PMO) according to the season attribute. The SVR model is trained by using the vector composed of historical flight bookings and adding up bookings of DCP in the early stage of the flight pre-sale period. Compared with the traditional models, the priori information of flight is increased. We collect 2 years of domestic route bookings data of an airline in China before COVID-19 as the training and testing datasets, and divide these data into three categories: tourism, business, and general, the numerical results show that the SVR model significantly improves the forecasting accuracy and reduces RMSE compared with the traditional models. Therefore, this study provides a better choice for flight demand forecasting.
RESUMO
As the COVID-19 pandemic evolves and new variants emerge, the development of more efficient identification approaches of variants is urgent to prevent continuous outbreaks of SARS-CoV-2. Field-effect transistors (FETs) with two-dimensional (2D) materials are viable platforms for the detection of virus nucleic acids (NAs) but cannot yet provide accurate information on NA variations. Herein, 2D Indium selenide (InSe) FETs were used to identify SARS-CoV-2 variants. The device's mobility and stability were ensured by atomic layer deposition (ALD) of Al2O3. The resulting FETs exhibited sub-fM detection limits ranging from 10(-14) M to 10(-)(8) M. The recognition of single-nucleotide variations was achieved within 15 min to enable the fast and direct identification of two core mutations (L452R, R203M) in Delta genomes (p < .01). Such capability originated from the trap states in oxidized InSe (InSe1-xOx) after ALD, resulting in traps-involved carrier transport responsive to the negative charges of NAs. In sum, the proposed approach might highly provide epidemiological information for timely surveillance of the COVID pandemic.