ALBERT

Description: Price is fundamental in the competitive strategy of lodgings. Determining whether a company is setting its prices appropriately in relation to its main competitors and customer expectations is essential in the new digital age. Online reputation is a way of measuring customer ratings and, when shared on the Internet, it generates expectations for future users. On the other hand, websites specializing in tourism constantly provide updated information about the prices offered by lodgings. The purpose of this study is to establish whether there is a relationship between price and the main variables of online reputation (perceived value, added value and perceived quality of service) as well as the function that best suits considering the category of accommodation, using the information available on the website Booking.com. The methodology applied is regression analysis using different functions (linear, logarithmic, inverse, quadratic and cubic). In addition, 4- and 5-star lodgings are analysed separately from those with 3 stars or less, concluding that there are significant differences between the variables that best explain the price, as well as the functions that best achieve this fit. In 4 and 5-star accommodations, the average quality of service variable is the one most related to prices, whereas in 3-star accommodations or less, the added value is the variable most related to prices. The cubic, quadratic and logarithmic functions get the best adjustments. The results obtained are of great interest to the management of the accommodation as customer ratings are linked to price levels in a competitive environment. This methodology facilitates the definition of the strategy and tactics of prices on the basis of real and updated market data, indicating in the conclusions the direct implication in the future development of learning machines and artificial intelligence applied to tourism.

Description: Context. Teegarden’s Star is the brightest and one of the nearest ultra-cool dwarfs in the solar neighbourhood. For its late spectral type (M7.0 V), the star shows relatively little activity and is a prime target for near-infrared radial velocity surveys such as CARMENES. Aims. As part of the CARMENES search for exoplanets around M dwarfs, we obtained more than 200 radial-velocity measurements of Teegarden’s Star and analysed them for planetary signals. Methods. We find periodic variability in the radial velocities of Teegarden’s Star. We also studied photometric measurements to rule out stellar brightness variations mimicking planetary signals. Results. We find evidence for two planet candidates, each with 1.1 M⊕ minimum mass, orbiting at periods of 4.91 and 11.4 d, respectively. No evidence for planetary transits could be found in archival and follow-up photometry. Small photometric variability is suggestive of slow rotation and old age. Conclusions. The two planets are among the lowest-mass planets discovered so far, and they are the first Earth-mass planets around an ultra-cool dwarf for which the masses have been determined using radial velocities.

Description: High oxidation potential as well as other advantages over other tertiary wastewater treatments have led in recent years to a focus on the development of advanced oxidation processes based on sulfate radicals (SR-AOPs). These radicals can be generated from peroxymonosulfate (PMS) and persulfate (PS) through various activation methods such as catalytic, radiation or thermal activation. This review manuscript aims to provide a state-of-the-art overview of the different methods for PS and PMS activaton, as well as the different applications of this technology in the field of water and wastewater treatment. Although its most widespread application is the elimination of micropollutants, its use for the disinfection of wastewater is gaining increasing interest. In addition, the possibility of combining this technology with ultrafiltration membranes to improve the water quality and lifespan of the membranes has also been discussed. Finally, a brief economic analysis of this technology has been undertaken and the different attempts made to implement it at full-scale have been summarized. As a result, this review tries to be useful for all those people working in that area.

Description: Context. The main goal of the CARMENES survey is to find Earth-mass planets around nearby M-dwarf stars. Seven M dwarfs included in the CARMENES sample had been observed before with HIRES and HARPS and either were reported to have one short period planetary companion (GJ 15 A, GJ 176, GJ 436, GJ 536 and GJ 1148) or are multiple planetary systems (GJ 581 and GJ 876). Aims. We aim to report new precise optical radial velocity measurements for these planet hosts and test the overall capabilities of CARMENES. Methods. We combined our CARMENES precise Doppler measurements with those available from HIRES and HARPS and derived new orbital parameters for the systems. Bona-fide single planet systems were fitted with a Keplerian model. The multiple planet systems were analyzed using a self-consistent dynamical model and their best fit orbits were tested for long-term stability. Results. We confirm or provide supportive arguments for planets around all the investigated stars except for GJ 15 A, for which we find that the post-discovery HIRES data and our CARMENES data do not show a signal at 11.4 days. Although we cannot confirm the super-Earth planet GJ 15 Ab, we show evidence for a possible long-period (Pc = 7030-630+970 d) Saturn-mass (mcsini = 51.8-5.8+5.5M⊕) planet around GJ 15 A. In addition, based on our CARMENES and HIRES data we discover a second planet around GJ 1148, for which we estimate a period Pc = 532.6-2.5+4.1 days, eccentricity ec = 0.342-0.062+0.050 and minimum mass mcsini = 68.1-2.2+4.9M⊕. Conclusions. The CARMENES optical radial velocities have similar precision and overall scatter when compared to the Doppler measurements conducted with HARPS and HIRES. We conclude that CARMENES is an instrument that is up to the challenge of discovering rocky planets around low-mass stars.

Description: The CARMENES radial velocity (RV) survey is observing 324 M dwarfs to search for any orbiting planets. In this paper, we present the survey sample by publishing one CARMENES spectrum for each M dwarf. These spectra cover the wavelength range 520–1710 nm at a resolution of at least R 〉80 000, and we measure its RV, Hα emission, and projected rotation velocity. We present an atlas of high-resolution M-dwarf spectra and compare the spectra to atmospheric models. To quantify the RV precision that can be achieved in low-mass stars over the CARMENES wavelength range, we analyze our empirical information on the RV precision from more than 6500 observations. We compare our high-resolution M-dwarf spectra to atmospheric models where we determine the spectroscopic RV information content, Q, and signal-to-noise ratio. We find that for all M-type dwarfs, the highest RV precision can be reached in the wavelength range 700–900 nm. Observations at longer wavelengths are equally precise only at the very latest spectral types (M8 and M9). We demonstrate that in this spectroscopic range, the large amount of absorption features compensates for the intrinsic faintness of an M7 star. To reach an RV precision of 1 m s−1 in very low mass M dwarfs at longer wavelengths likely requires the use of a 10 m class telescope. For spectral types M6 and earlier, the combination of a red visual and a near-infrared spectrograph is ideal to search for low-mass planets and to distinguish between planets and stellar variability. At a 4 m class telescope, an instrument like CARMENES has the potential to push the RV precision well below the typical jitter level of 3–4 m s−1.

Description: We report on the first star discovered to host a planet detected by radial velocity (RV) observations obtained within the CARMENES survey for exoplanets around M dwarfs. HD 147379 (V = 8.9 mag, M = 0.58 ± 0.08 M⊙), a bright M0.0 V star at a distance of 10.7 pc, is found to undergo periodic RV variations with a semi-amplitude of K = 5.1 ± 0.4 m s−1 and a period of P = 86.54 ± 0.06 d. The RV signal is found in our CARMENES data, which were taken between 2016 and 2017, and is supported by HIRES/Keck observations that were obtained since 2000. The RV variations are interpreted as resulting from a planet of minimum mass mP sin i = 25 ± 2 M⊕, 1.5 times the mass of Neptune, with an orbital semi-major axis a = 0.32 au and low eccentricity (e 〈 0.13). HD 147379 b is orbiting inside the temperate zone around the star, where water could exist in liquid form. The RV time-series and various spectroscopic indicators show additional hints of variations at an approximate period of 21.1 d (and its first harmonic), which we attribute to the rotation period of the star.

Description: In this article, a technique for the reduction of total harmonic distortion (THD) in distributed renewables energy access (DREA) composed of wind turbines is introduced and tested under the wind speed conditions presented in Tamaulipas, Mexico. The analysis and simulation are delimited by a study case based on wind speeds measured and recorded for one year at two highs in the municipality of Soto La Marina, Tamaulipas, Mexico. From this information, the most probable wind speed and the corresponding turbulence intensity is calculated and applied to a wind energy conversion system (WECS). The WECS is composed of an active front-end (AFE) converter topology using four voltage source converters (VSCs) connected in parallel with a different phase shift angle at the digital sinusoidal pulse width modulation (DSPWM) signals of each VSC. The WECS is formed by the connection of five type-4 wind turbines (WTs). The effectiveness and robustness of the DREA integration are reviewed in the light of a complete mathematical model and corroborated by the simulation results in Matlab-Simulink®. The results evidence a reduction of the THD in grid currents up to four times and which enables the delivery of a power capacity of 10 MVA in the Tamaulipas AC distribution grid that complies with grid code of harmonic distortion production.

Description: Machine learning techniques combined with wearable electronics can deliver accurate short-term blood glucose level prediction models. These models can learn personalized glucose–insulin dynamics based on the sensor data collected by monitoring several aspects of the physiological condition and daily activity of an individual. Until now, the prevalent approach for developing data-driven prediction models was to collect as much data as possible to help physicians and patients optimally adjust therapy. The objective of this work was to investigate the minimum data variety, volume, and velocity required to create accurate person-centric short-term prediction models. We developed a series of these models using different machine learning time series forecasting techniques suitable for execution within a wearable processor. We conducted an extensive passive patient monitoring study in real-world conditions to build an appropriate data set. The study involved a subset of type 1 diabetic subjects wearing a flash glucose monitoring system. We comparatively and quantitatively evaluated the performance of the developed data-driven prediction models and the corresponding machine learning techniques. Our results indicate that very accurate short-term prediction can be achieved by only monitoring interstitial glucose data over a very short time period and using a low sampling frequency. The models developed can predict glucose levels within a 15-min horizon with an average error as low as 15.43 mg/dL using only 24 historic values collected within a period of sex hours, and by increasing the sampling frequency to include 72 values, the average error is reduced to 10.15 mg/dL. Our prediction models are suitable for execution within a wearable device, requiring the minimum hardware requirements while at simultaneously achieving very high prediction accuracy.

Description: Type 1 Diabetes Mellitus (DM1) patients are used to checking their blood glucose levels several times per day through finger sticks and, by subjectively handling this information, to try to predict their future glycaemia in order to choose a proper strategy to keep their glucose levels under control, in terms of insulin dosages and other factors. However, recent Internet of Things (IoT) devices and novel biosensors have allowed the continuous collection of the value of the glucose level by means of Continuous Glucose Monitoring (CGM) so that, with the proper Machine Learning (ML) algorithms, glucose evolution can be modeled, thus permitting a forecast of this variable. On the other hand, glycaemia dynamics require that such a model be user-centric and should be recalculated continuously in order to reflect the exact status of the patient, i.e., an ‘on-the-fly’ approach. In order to avoid, for example, the risk of being disconnected from the Internet, it would be ideal if this task could be performed locally in constrained devices like smartphones, but this would only be feasible if the execution times were fast enough. Therefore, in order to analyze if such a possibility is viable or not, an extensive, passive, CGM study has been carried out with 25 DM1 patients in order to build a solid dataset. Then, some well-known univariate algorithms have been executed in a desktop computer (as a reference) and two constrained devices: a smartphone and a Raspberry Pi, taking into account only past glycaemia data to forecast glucose levels. The results indicate that it is possible to forecast, in a smartphone, a 15-min horizon with a Root Mean Squared Error (RMSE) of 11.65 mg/dL in just 16.15 s, employing a 10-min sampling of the past 6 h of data and the Random Forest algorithm. With the Raspberry Pi, the computational effort increases to 56.49 s assuming the previously mentioned parameters, but this can be improved to 34.89 s if Support Vector Machines are applied, achieving in this case an RMSE of 19.90 mg/dL. Thus, this paper concludes that local on-the-fly forecasting of glycaemia would be affordable with constrained devices.