Class Of Stars Research Articles

Strategic orientation of hotels: Evidence from a contingent approach

Strategic orientation (SO) – a necessary condition to achieve and maintain a competitive advantage – should particularly be considered in the tourism industry, given the high competition and rapidly changing environment. This article investigates whether the different dimensions of SO (entrepreneurial orientation (EO), learning orientation (LO) and market orientation (MO)) have a direct effect on hotel performance and whether the relationship between SO and hotel performance is contingent on various hotel-specific characteristics. The hypotheses are tested against a sample of 120 small hotels operating in a mature Italian tourist destination. The results show that EO and MO are positive drivers of hotel performance, and LO is not important. Additionally, the intensity of the relationship between SO and performance is contingent on internal firm-related moderators (size and quality). Both the number of rooms and the star classification reinforce the performance achievement of innovative and customer-oriented hotels.

Stellar spectral classification and feature evaluation based on a random forest

With the availability of multi-object spectrometers and the design and operation of some large scale sky surveys, the issue of how to deal with enormous quantities of spectral data efficiently and accurately is becoming more and more important. This work investigates the classification problem of stellar spectra under the assumption that there is no perfect absolute flux calibration, for example, when considering spectra from the Guo Shou Jing Telescope (the Large Sky Area Multi-Object Fiber Spectroscopic Telescope, LAMOST). The proposed scheme consists of the following two procedures: Firstly, a spectrum is normalized based on a 17th order polynomial fitting; secondly, a random forest (RF) is utilized to classify the stellar spectra. Experiments on four stellar spectral libraries show that the RF has good classification performance. This work also studied the spectral feature evaluation problem based on RF. The evaluation is helpful in understanding the results of the proposed stellar classification scheme and exploring its potential improvements in the future.

SKILL MISMATCH DAN UPAYA MENGATASINYA (Studi pada 3 hotel di Semarang)

This study aims to explain how to overcome the employee skill mismatch that works with the company. The research was conducted at the hotel because of the high of its business competition and the gap between the number of hospitality education institutions and the number of hotels in operation that had consequences for the occurrence of skill mismatch. The findings of a previous study of skill mismatch concluded that there was a disruption in competitiveness and productivity in companies using employees with skills and education gaps. Using a multi-case study technique, data collection was carried out in five-star hotels, three-star hotels and one-month period June-August 2018 in Semarang. The results of the study show that the effort to overcome the mismatch skill in the company is to conduct employee learning. There are differences in the learning process of employees in overcoming skill mismatch, where the higher the star classification in the hotel business the higher the involvement of the company. In other words, the smaller the classification of hotel stars, the individual efforts of employees to learn to overcome the mismatch skill is also greater. Based on these findings, this study uses the learning organization theory as a frame of mind and analysis.

The Classifications of Double Neutron Stars and their Correlations with the Binary Orbital Parameters

The statistical distribution of the total mass of 14 double neutron star (DNS) systems (out of 18 pairs observed) is found to be very narrow, with the average and standard deviation as 2.65 ± 0.03 M⊙. For the seven pairs of DNSs with the precisely measured masses of the recycled NSs (first formed) and their non-recycled companions (second formed), their masses are homogeneously distributed as 1.38 ± 0.02 M⊙ and 1.29 ± 0.02 M⊙, respectively. It is shown by the Anderson–Darling and Mann–Whitney–Wilcoxon tests that the first-born NSs share a same distribution, which should be formed by the supernova explosion; however, the second-formed NSs have the different statistical origins, which can be ascribed to three formation mechanisms after the further investigations of their orbital parameters and mass values, for instance, supernova explosion (e.g., PSR B1913+16), electron capture (e.g., PSR J0737-3039B), and ultra-stripped cores in close-orbit systems (e.g., PSR J1411+2551). In addition, by means of Fisher’s discriminant method, through simulation of the two groups of DNS samples that originated from electron-capture supernova and core-collapse supernova, we obtained the linear discriminant equation (e = −4.5Porb(day) + 1.1) between the eccentricity and orbital period, which can divide the DNSs into two groups involved in NS formed by electron-capture supernova (core-collapse supernova) without (with) kick.

PTF14jg: The Remarkable Outburst and Post-burst Evolution of a Previously Anonymous Galactic Star

Abstract We report the outbursting source PTF 14jg, which, prior to the onset of its late 2013 eruption, was a faint, unstudied, and virtually uncataloged star. The salient features of the PTF 14jg outburst are (i) projected location near the W4 H ii region and radial velocity consistent with physical association; (ii) a light curve that underwent an ∼6–7 mag optical (R-band) through mid-infrared (L-band) brightening on a timescale of a few months, peaked and then faded by ∼3 mag, but plateaued still >3.5 mag above quiescence by ∼8 months post-peak, lasting to at least 4 yr after eruption; (iii) strong outflow signatures with velocities reaching −530 ; (iv) a low-gravity and broad (∼100–150 FWHM) optical absorption-line spectrum that systematically changes its spectral type with wavelength; (v) lithium; and (vi) ultraviolet and infrared excess. We tentatively identify the outburst as exhibiting characteristics of a young star FU Ori event. However, the burst would be unusually hot, with an absorption spectrum exhibiting high-excitation (∼11,000–15,000 K) lines in the optical and no evidence of CO in the near-infrared, in addition to exhibiting an unusual light curve. We thus also consider alternative scenarios—including various forms of novae, nuclear-burning instabilities, massive star events, and mergers—finding them all inferior to the atypically hot FU Ori star classification. The source eventually may be interpreted as a new category of young star outburst with a larger amplitude and shorter rise time than most FU Ori–like events.

The relationship between sustainability and customer satisfaction in hospitality: An explorative investigation using eWOM as a data source

Sustainability is a global megatrend and has gained increasing attention in the hospitality industry. To facilitate sustainable development, it is important to understand how sustainability relates to customer satisfaction. Previous studies do not consider specific sustainability measures in hospitality and neglect eWOM as an objective data source.This study investigates the extent to which sustainability aspects play a role in eWOM and how sustainability orientation in general as well as specific sustainability measures are linked to customer satisfaction. The results indicate that although only a few online reviews contain sustainability aspects, there is a relationship between sustainability orientation and customer satisfaction that is moderated by star classification. Furthermore, the results show that the relationship differs depending on the specific sustainability measure. Among the observed measures, "dissatisfiers" as well as "satisfiers", "criticals" and "neutrals" are found, supporting Cadotte and Turgeon's categorization of attributes in the context of sustainability.

Young open cluster IC 4996 and its vicinity: multicolor photometry and Gaia DR2 astrometry

The open cluster IC 4996 in Cygnus and its vicinity are investigated by applying a two-dimensional photometric classification of stars measured in the Vilnius seven-color photometric system. Cluster members are identified by applying distances based on the Gaia DR2 parallaxes and the point vector diagram of the Gaia DR2 proper motions. For some B-type stars, spectroscopic MK types are also obtained from the Asiago spectra and collected from the literature. New parameters of the cluster are derived. The interstellar extinction AV covers a wide range of values, from 1.3 to 2.4 mag; the mean value in the central part of the cluster is 1.8 mag. The cluster distance is 1915 ± 110 pc, and its age is within 8–10 Myr. The cluster exhibits a long sequence from early-B to G stars, where stars cooler than B8 are in the pre-main-sequence stage. The plot of extinction versus distance shows a steep rise of AV up to 1.6 mag at 700–800 pc, which is probably related to dust clouds at the edge of the Great Cygnus Rift. The next increase in extinction by an additional 0.8 mag at d ≥ 1.7 kpc is probably related to the associations Cyg OB1 and Cyg OB3. The cluster IC 4996 does not belong to the Cyg OB1 association, which is located closer to the Sun, at 1682 ± 116 pc. It seems likely that the cluster and the surrounding O-B stars have a common origin with the nearby association Cyg OB3 since Gaia data show that these stellar groups are located at a similar distance.

Calibrating long-period variables as standard candles with machine learning

Variable stars with well-calibrated period-luminosity relationships provide accurate distance measurements to nearby galaxies and are therefore a vital tool for cosmology and astrophysics. While these measurements typically rely on samples of Cepheid and RR-Lyrae stars, abundant populations of luminous variable stars with longer periods of $10 - 1000$ days remain largely unused. We apply machine learning to derive a mapping between lightcurve features of these variable stars and their magnitude to extend the traditional period-luminosity (PL) relation commonly used for Cepheid samples. Using photometric data for long period variable stars in the Large Magellanic cloud (LMC), we demonstrate that our predictions produce residual errors comparable to those obtained on the corresponding Cepheid population. We show that our model generalizes well to other samples by performing a blind test on photometric data from the Small Magellanic Cloud (SMC). Our predictions on the SMC again show small residual errors and biases, comparable to results that employ PL relations fitted on Cepheid samples. The residual biases are complementary between the long period variable and Cepheid fits, which provides exciting prospects to better control sources of systematic error in cosmological distance measurements. We finally show that the proposed methodology can be used to optimize samples of variable stars as standard candles independent of any prior variable star classification.

An Algorithm for the Visualization of Relevant Patterns in Astronomical Light Curves

Within the last years, the classification of variable stars with machine learning has become a mainstream area of research. Recently, visualization of time series is attracting more attention in data science as a tool to visually help scientists to recognize significant patterns in complex dynamics. Within the machine learning literature, dictionary-based methods have been widely used to encode relevant parts of image data. These methods intrinsically assign a degree of importance to patches in pictures, according to their contribution in the image reconstruction. Inspired by dictionary-based techniques, we present an approach that naturally provides the visualization of salient parts in astronomical light curves, making the analogy between image patches and relevant pieces in time series. Our approach encodes the most meaningful patterns such that we can approximately reconstruct light curves by just using the encoded information. We test our method in light curves from the ogle-iii and starlight data bases. Our results show that the proposed model delivers an automatic and intuitive visualization of relevant light curve parts, such as local peaks and drops in magnitude.

SKILL MISMATCH DAN UPAYA MENGATASINYA (Studi pada 3 hotel di Semarang)

This study aims to explain how to overcome the employee skill mismatch that works with the company. The research was conducted at the hotel because of the high of its business competition and the gap between the number of hospitality education institutions and the number of hotels in operation that had consequences for the occurrence of skill mismatch. The findings of a previous study of skill mismatch concluded that there was a disruption in competitiveness and productivity in companies using employees with skills and education gaps. Using a multi-case study technique, data collection was carried out in five-star hotels, three-star hotels and one-month period June-August 2018 in Semarang. The results of the study show that the effort to overcome the mismatch skill in the company is to conduct employee learning. There are differences in the learning process of employees in overcoming skill mismatch, where the higher the star classification in the hotel business the higher the involvement of the company. In other words, the smaller the classification of hotel stars, the individual efforts of employees to learn to overcome the mismatch skill is also greater. Based on these findings, this study uses the learning organization theory as a frame of mind and analysis.

A machine learning approach for identification and classification of symbiotic stars using 2MASS and WISE

In this second paper in a series of papers based on the most-up-to-date catalogue of symbiotic stars (SySts), we present a new approach for identifying and distinguishing SySts from other Halpha emitters in photometric surveys using machine learning algorithms such as classification tree, linear discriminant analysis, and K-nearest neighbour. The motivation behind of this work is to seek for possible colour indices in the regime of near- and mid-infrared covered by the 2MASS and WISE surveys. A number of diagnostic colour-colour diagrams are generated for all the known Galactic SySts and several classes of stellar objects that mimic SySts such as planetary nebulae, post-AGB, Mira, single K and M giants, cataclysmic variables, Be, AeBe, YSO, weak and classical T Tauri stars, and Wolf-Rayet. The classification tree algorithm unveils that primarily J-H, W1-W4 and Ks-W3 and secondarily H-W2, W1-W2 and W3-W4 are ideal colour indices to identify SySts. Linear discriminant analysis method is also applied to determine the linear combination of 2MASS and AllWISE magnitudes that better distinguish SySts. The probability of a source being a SySt is determined using the K-nearest neighbour method on the LDA components. By applying our classification tree model to the list of candidate SySts (Paper I), the IPHAS list of candidate SySts, and the DR2 VPHAS+ catalogue, we find 125 (72 new candidates) sources that pass our criteria while we also recover 90 per cent of the known Galactic SySts.

The Spectral Classification of Stars over the last 200, 100, 75 years and in the future

AbstractThis year 2018 has great historical and current significance for stellar spectral classification. Two hundred years ago in Reggio Emilia, Italy, was born Angelo Secchi, a pioneer of observing and classifying the spectra of stars. At the beginning of the IAU, almost a hundred years ago, one of its original Commissions was entitled the Spectral Classification of Stars, from which was generated Commission 45, Spectral Classification and Multi-band Colour Indices. And seventy-five years ago, was published the system-changing MKK, An Atlas of Stellar Spectra. Through this necessarily brief, historical view we shall recall how spectral classification, supported internationally by the IAU, continually updated its techniques, while remaining anchored to standards. This has ensured that the MK classification process stays very relevant to the initial characterizing of stars in the 21st century era of large spectral surveys.

Deep multi-survey classification of variable stars

During the last decade, a considerable amount of effort has been made to classify variable stars using different machine learning techniques. Typically, light curves are represented as vectors of statistical descriptors or features that are used to train various algorithms. These features demand big computational powers that can last from hours to days, making impossible to create scalable and efficient ways of automatically classifying variable stars. Also, light curves from different surveys cannot be integrated and analyzed together when using features, because of observational differences. For example, having variations in cadence and filters, feature distributions become biased and require expensive data-calibration models. The vast amount of data that will be generated soon make necessary to develop scalable machine learning architectures without expensive integration techniques. Convolutional Neural Networks have shown impressing results in raw image classification and representation within the machine learning literature. In this work, we present a novel Deep Learning model for light curve classification, mainly based on convolutional units. Our architecture receives as input the differences between time and magnitude of light curves. It captures the essential classification patterns regardless of cadence and filter. In addition, we introduce a novel data augmentation schema for unevenly sampled time series. We test our method using three different surveys: OGLE-III; Corot; and VVV, which differ in filters, cadence, and area of the sky. We show that besides the benefit of scalability, our model obtains state of the art levels accuracy in light curve classification benchmarks.

The PAU survey: star–galaxy classification with multi narrow-band data

Classification of stars and galaxies is a well-known astronomical problem that has been treated using different approaches, most of them relying on morphological information. In this paper, we tackle this issue using the low-resolution spectra from narrow band photometry, provided by the PAUS (Physics of the Accelerating Universe) survey. We find that, with the photometric fluxes from the 40 narrow band filters and without including morphological information, it is possible to separate stars and galaxies to very high precision, 98.4% purity with a completeness of 98.8% for objects brighter than I = 22.5. This precision is obtained with a Convolutional Neural Network as a classification algorithm, applied to the objects' spectra. We have also applied the method to the ALHAMBRA photometric survey and we provide an updated classification for its Gold sample.

An atlas of cool supergiants from the Magellanic Clouds and typical interlopers

We present an atlas composed of more than 1500 spectra of late-type stars (spectral types from G to M) observed simultaneously in the optical and calcium triplet spectral ranges. These spectra were obtained as part of a survey to search for cool supergiants in the Magellanic Clouds and were taken over four epochs. We provide the spectral and luminosity classification for each spectrum (71% are supergiants, 13% are giants or luminous giants, 4% are carbon or S stars, and the remaining 12% are foreground stars of lesser luminosities). We also provide a detailed guide for the spectral classification of luminous late-type stars, the result of the extensive classification work done for the atlas. Although this guide is based on classical criteria, we have put them together and re-elaborated them for modern CCD-spectra as these criteria were scattered among many different works and mainly conceived for use with photographic plate spectra. The result is a systematic, well-tested process for identifying and classifying luminous late-type stars, illustrated with CCD spectra of standard stars and the classifications of our own catalogue.

New calibration of the Vilnius photometric system

AbstractThe medium-band Vilnius photometric system with the mean wavelengths at 345 (U), 374 (P), 405 (X), 466 (Y), 516 (Z), 544 (V), and 656 (S) nm for many years was an important tool to determine interstellar reddenings and distances of single stars due to its ability to classify stars of all temperatures in spectral classes and luminosity classes in the presence of different interstellar reddenings. At present, Gaia DR2 presents distances to stars with an unprecedented accuracy at least up to 3 kpc. However, multicolor photometry, which allows the classification of stars as well as the preliminary determination of stellar temperatures, gravities, metallicities and interstellar reddenings, remains an important method for distant stars. Here we present an empirical calibration of the intrinsic color indices of the Vilnius system in terms of physical parameters of stars for dwarf and giant stars of spectral classes F-G-K-M. In any attempted photometric determination of physical parameters of stars it is important to have an extensive and homogeneous sample of spectroscopically determined parameters for stars for which there are also accurate photometric data. As a source catalogue for the Vilnius photometry the latest updated version of the Catalogue of Photoelectric Observations in the Vilnius System was used, which contains compilations from the published photometry for about 11 000 stars. The stars which had both the Gaia DR2 parallaxes and the determinations of stellar parameters from high-dispersion spectra were extracted from this catalogue. The final sample contains more than 1500 stars of spectral classes F-M. The majority of these stars (ca 70%) are not reddened, for others the values of interstellar reddening AV were determined using the regular techniques of photometric classification in the Vilnius system. The absolute magnitudes MV and consequently the luminosity classes were determined using Gaia DR2 parallaxes. We present the analytical expressions for the effective temperature Teff and surface gravity logg and evaluate the errors of solutions for dwarf and giant stars. To test the accuracy of the proposed method, we have compared our results with the stars observed by Gaia and with the stellar parameters available from the large spectroscopic surveys: APOGEE, Gaia-ESO, GALAH, LAMOST, RAVE and SEGUE. The results of comparison contain 5-6 % outliers.The proposed method allows the fast and straightforward evaluation of stellar physical parameters for the stars observed in the Vilnius photometric system. Despite the fact, that the accuracy of determination is significantly lower than in the case of spectroscopic methods, the method described may be useful for distant faint stars, which are still inaccessible for spectroscopic observations.

On the use of logistic regression for stellar classification

We are totally immersed in the Big Data era and reliable algorithms and methods for data classification are instrumental for astronomical research. Random Forest and Support Vector Machines algorithms have become popular over the last few years and they are widely used for different stellar classification problems. In this article, we explore an alternative supervised classification method scarcely exploited in astronomy, Logistic Regression, that has been applied successfully in other scientific areas, particularly biostatistics. We have applied this method in order to derive membership probabilities for potential T Tauri star candidates from ultraviolet-infrared colour-colour diagrams.

Classification of stellar spectra with SVM based on within-class scatter and between-class scatter

Support Vector Machine (SVM) is a popular data mining technique, and it has been widely applied in astronomical tasks, especially in stellar spectra classification. Since SVM doesn’t take the data distribution into consideration, and therefore, its classification efficiencies can’t be greatly improved. Meanwhile, SVM ignores the internal information of the training dataset, such as the within-class structure and between-class structure. In view of this, we propose a new classification algorithm-SVM based on Within-Class Scatter and Between-Class Scatter (WBS-SVM) in this paper. WBS-SVM tries to find an optimal hyperplane to separate two classes. The difference is that it incorporates minimum within-class scatter and maximum between-class scatter in Linear Discriminant Analysis (LDA) into SVM. These two scatters represent the distributions of the training dataset, and the optimization of WBS-SVM ensures the samples in the same class are as close as possible and the samples in different classes are as far as possible. Experiments on the K-, F-, G-type stellar spectra from Sloan Digital Sky Survey (SDSS), Data Release 8 show that our proposed WBS-SVM can greatly improve the classification accuracies.

Legionella spp. Risk Assessment in Recreational and Garden Areas of Hotels.

Several Travel-associated Legionnaires’ disease (TALD) cases occur annually in Europe. Except from the most obvious sites (cooling towers and hot water systems), infections can also be associated with recreational, water feature, and garden areas of hotels. This argument is of great interest to better comprehend the colonization and to calculate the risk to human health of these sites. From July 2000–November 2017, the public health authorities of the Island of Crete (Greece) inspected 119 hotels associated with TALD, as reported through the European Legionnaires’ Disease Surveillance Network. Five hundred and eighteen samples were collected from decorative fountain ponds, showers near pools and spas, swimming pools, spa pools, garden sprinklers, drip irrigation systems (reclaimed water) and soil. Of those, 67 (12.93%), originating from 43 (35.83%) hotels, tested positive for Legionella (Legionella pneumophila serogroups 1, 2, 3, 6, 7, 8, 13, 14, 15 and non-pneumophila species (L. anisa, L. erythra, L. taurinensis, L. birminghamensis, L. rubrilucens). A Relative Risk (R.R.) > 1 (p < 0.0001) was calculated for chlorine concentrations of less than 0.2 mg/L (R.R.: 54.78), star classification (<4) (R.R.: 4.75) and absence of Water Safety Plan implementation (R.R.: 3.96). High risk (≥104 CFU/L) was estimated for pool showers (16.42%), garden sprinklers (7.46%) and pool water (5.97%).

Variability search in M 31 using principal component analysis and the Hubble Source Catalogue

Principal Component Analysis (PCA) is being extensively used in Astronomy but not yet exhaustively exploited for variability search. The aim of this work is to investigate the effectiveness of using the PCA as a method to search for variable stars in large photometric data sets. We apply PCA to variability indices computed for light curves of 18152 stars in three fields in M 31 extracted from the Hubble Source Catalogue. The projection of the data into the principal components is used as a stellar variability detection and classification tool, capable of distinguishing between RR Lyrae stars, long period variables (LPVs) and non-variables. This projection recovered more than 90% of the known variables and revealed 38 previously unknown variable stars (about 30% more), all LPVs except for one object of uncertain variability type. We conclude that this methodology can indeed successfully identify candidate variable stars.