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Four B stars, Alpha Vir, Beta Cen, Alpha Gru, and Beta Lib, were observed with the Copernicus spectrometer at a resolution of 0.1 A in order to investigate the presence of chromospheric emission. Emission was observed in Beta Cen and Alpha Gru, while the results for Alpha Vir and Beta Lib were inconclusive.
Using the conditional LOGIT model, this paper addresses the airport choice in the Sao Paulo Metropolitan Area. In this region, Guarulhos International Airport (GRU) and Congonhas Airport (CGH) compete for passengers flying to several domestic destinations. The airport choice is believed to be a result of the tradeoff passengers perform considering airport access characteristics, airline level of service characteristics and passenger experience with the analyzed airports. It was found that access time to the airports better explain the airport choice than access distance, whereas direct flight frequencies gives better explanation to the airport choice than the indirect (connections and stops) and total (direct plus indirect) flight frequencies. Out of 15 tested variables, passenger experience with the analyzed airports was the variable that best explained the airport choice in the region. Model specifications considering 1, 2 or 3 variables were tested. The model specification most adjusted to the observed data considered access time, direct flight frequencies in the travel period (morning or afternoon peak) and passenger experience with the analyzed airports. The influence of these variables was therefore analyzed across market segments according to departure airport and flight duration criteria. The choice of GRU (located neighboring Sao Paulo city) is not well explained by the rationality of access time economy and the increase of the supply of direct flight frequencies, while the choice of CGH (located inside Sao Paulo city) is. Access time was found to be more important to passengers flying shorter distances while direct flight frequencies in the travel period were more significant to those flying longer distances. Keywords: Airport choice, Multiple airport region, Conditional LOGIT model, Access time, Flight frequencies, Passenger experience with the analyzed airports, Transportation planning
Observations alpha Tau (K5III) and beta Gru (M2II) made at high resolution are used to confirm line identifications of features blended at low resolution. The high resolution spectra allow selected pairs of lines to be used to find the electron density and the opacity. Methods for determining these factors and the usual emission measure are presented. The electron density and opacity can be used together with the emission measure to place constraints on the structure of the atmosphere. The line formation processes are briefly discussed. Photo-excitation by strong lines appears to be important in these late type atmospheres.
Copernicus and IUE observations of 5 stars within 50 pc of the Sun were combined to study the ionization of magnesium in the local interstellar medium (LISM). The high resolution Copernicus spectrometer was used to detect interstellar MG I 2852 in the spectra of alpha Gru, alpha Eri, and alpha Lyr, while placing upper limits on Mg I in the spectra of alpha CMa and alpha PsA. Observations of Mg II 2795, 2802 for these stars were also obtained with IUE and Copernicus. The column densities of Mg I and Mg II are used to place constraints on the temperature of the LISM.
Using the conditional (multinomial) LOGIT model, this paper addresses airline choice in the S o Paulo Metropolitan Area. There are two airports in this region, where two, three or even four airlines compete for passengers flying to an array of domestic destinations. The airline choice is believed to be a result of the tradeoff passengers face among flight cost, flight frequency and airline performance. It was found that the lowest fare better explains airline choice than the highest fare, whereas direct flight frequencies give better explanation to airline choice than indirect (connections and stops) and total (direct plus indirect) ones. Out of 15 variables tested, the lowest fare was the variable that best explained airline choice. However, its signal was counterintuitive (positive) possibly because the cheapest airline was offering few flights, so passengers overwhelmingly failed to choose the cheapest airline. The model specification most adjusted to the data considered the lowest fare, direct flight frequency in the travel day and period (morning or afternoon peak) and airline age. Passengers departing from S o Paulo-Guarulhos International Airport (GRU) airport make their airline choice in terms of cost whereas those from Sao Paulo-Congonhas Airport (CGH) airport do not. Finally, senior passengers place more importance on airline age than junior passengers.
The development of future technologies for the National Airspace System (NAS) will be reliant on a new communications infrastructure capable of managing the limited available spectrum for communications among aircraft and ground systems. Emerging approaches to autonomous allocation of aviation spectrum mostlyrely on machine learning techniques, where 4D (longitude, latitude, altitude, time) trajectory prediction is an important data input to enable real-time resource allocation. This study explores and evaluates effective data sources and deep recurrent neural network techniques when determining flight trajectories. Specifically, data are collected and evaluated in a 100-day and 14-day period. Sources of data include NASA Sherlock Data Warehouse, MIT Lincoln Labs Corridor Integrated Weather Service (CIWS), and assorted NOAA weather datasets. Deep learning models for 4D predictions all utilize a hybrid-recurrent technique. A baseline model is considered via the convolutional-LSTM design from the existing literature. The modified design considers Gated Recurrent Units (GRU), Independently Recurrent Neural Networks (IndRNN), and stand-alone self-attention layers. Results indicatethe effectiveness of LSTM and GRUcells for state-of-the-art data processing (interpolation). Additionally, GRUs may be quickly trained with limited data, allowing for exacting improvements with optimizer selection. Attention mechanisms provide notable performance improvements to convolutional layers and may extend dimensional capabilities of a learning model. Finally, NOAA measurements provide only a supplemental value, requiring support from tailored measurements for Air Traffic Management.
Land surface models are crucial tools for many earth science applications including numerical weather prediction, water resource and crop monitoring, and climatological analysis. Given a set of atmospheric forcings, seasonal data, and static parameters, models like Noah-LSM solve for land surface quantities including skin temperature, sensible heat flux, and soil moisture. While these calculations are theoretically robust, they are often computationally expensive. Since artificial neural networks (ANNs) are universal function approximators, they can learn to emulate the output of a deterministic numerical model given a time series of input forcings, with the learned ANN having substantially shorter execution time. The ANN could efficiently parameterize other models, generate ensembles, and provide first-guess inputs for retrievals. As such, with the goal of developing a model that efficiently mimics the output of Noah-LSM given NLDAS2 forcings on a region covering much of the central US, we examine and compare several neural network architectures for the multi-horizon multivariate time series forecasting problem. Recent literature includes a diverse set of approaches including autoregressive architectures like LSTM and GRU, parametric and non-parametric statistical predictors (ForecastNet and MQRNN), self-attention (LSTM-attention-LSTM), and temporal convovlution (DeepTCN). We implement several of these models for the Noah-LSM prediction task, highlighting the features and challenges for each and providing practical insight on the training process.