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: Sustainability, Vol. 10, Pages 78: A Model of Market Positioning of Destinations Based on Online Customer Reviews of Lodgings Sustainability doi: 10.3390/su10010078 Authors: Manuel Rodríguez-Díaz Rosa Rodríguez-Díaz Ana Rodríguez-Voltes Crina Rodríguez-Voltes The aim of this study is to develop a methodology to determine the competitive online positioning of lodging companies in different tourist destinations. The rise of the digital age has allowed many customers to share their opinions through specialized websites, providing a dynamic and constantly updated evaluation of the market. In this context, competitiveness is an essential factor in the economic sustainability of destinations. The competitive positioning of destinations is determined by the scale of variables used by Booking.com. The price and lodging category variables are also used, as well as three new variables derived from the initial scale: the quality average, value and added value. This methodology provides a tool to determine the level of competitiveness of the lodging offered in tourist destinations, based on which, actions can be taken to improve destinations’ positioning.

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: Single-nucleotide polymorphisms (SNPs) in the protein phosphatase and actin regulator 1 gene (PHACTR1) have been associated with susceptibility to develop several diseases, including cardiovascular disease. The purpose of this study was to evaluate the role of two polymorphisms (rs2026458 and rs9349379) of the PHACTR1 gene in the susceptibility to the risk of developing premature coronary artery disease (CAD) in the Mexican population. The genotype analysis was performed using 5’exonuclease TaqMan genotyping assays in a group of 994 patients with premature CAD and 703 controls. A similar genotype distribution of rs2026458 was observed in both groups; however, under an additive model adjusted by age, body mass index, type 2 diabetes mellitus, smoking, dyslipidemia, and hypertension, the rs9349379 G allele was associated with a higher risk for developing premature CAD (odds ratio (OR) = 1.22, 95% confidence interval (CI) = 1.03–1.46, p-value (p) = 0.024). The two PHACTR1 polymorphisms were not in linkage disequilibrium. In summary, our results suggest that the PHACTR1 rs9349379 polymorphism plays an important role in the risk of developing premature CAD in the Mexican population.

Description: Sustainability, Vol. 10, Pages 2789: In Vivo Cytotoxicity Induced by 60 Hz Electromagnetic Fields under a High-Voltage Substation Environment Sustainability doi: 10.3390/su10082789 Authors: J. Antonio Heredia-Rojas Abraham Octavio Rodríguez-De la Fuente Ricardo Gomez-Flores Omar Heredia-Rodríguez Laura E. Rodríguez-Flores Michaela Beltcheva Ma. Esperanza Castañeda-Garza Living beings permanently receive electromagnetic radiation, particularly from extremely low-frequency electromagnetic fields (ELF-EMFs), which may cause adverse health effects. In this work, we studied the in vivo cytotoxic effects of exposing BALB/c mice to 60 Hz and 8.8 µT EMFs during 72 h and 240 h in a switchyard area, using animals exposed to 60 Hz and 2.0 mT EMFs or treated with 5 mg/kg mitomycin C (MMC) as positive controls. Micronucleus (MN) frequency and male germ cell analyses were used as cytological endpoints. ELF-EMF exposure was observed to significantly (p < 0.05) increase MN frequency at all conditions tested, with the 2 mT/72 h treatment causing the highest response, as compared with untreated control. In addition, increased sperm counts were observed after switchyard area ELF-EMF exposure, as compared with untreated control. In contrast, low sperm counts were obtained for 72 h/2.0 mT-exposed animals and for MMC-treated mice (p < 0.05), without altering male germ cell morphological characteristics.

Description: Sustainability, Vol. 10, Pages 1603: Gap Analysis of the Online Reputation Sustainability doi: 10.3390/su10051603 Authors: Manuel Rodríguez-Díaz Crina Isabel Rodríguez-Voltes Ana Cristina Rodríguez-Voltes Online reputation is a strategic element of firms’ competitiveness. Companies need to manage their reputations and the image that they communicate through the Internet. This paper proposes a model to determine the main aspects that define a competitive online reputation: coherence, veracity, and intensity. The traditional methods that have been used to determine service quality must be adapted to new digital developments and their effects on customers’ behavior. Therefore, a gap analysis is performed to define the key aspects that must be managed in order to create and maintain a powerful reputation and image in the companies’ communication. Since this subject is too complex to be implemented in distinct sectors and ambits, different lines of research are proposed to expand this new critical line of study.

Description: Sensors, Vol. 17, Pages 2749: Mathematical Model for Localised and Surface Heat Flux of the Human Body Obtained from Measurements Performed with a Calorimetry Minisensor Sensors doi: 10.3390/s17122749 Authors: Fabiola Socorro Pedro Rodríguez de Rivera Miriam Rodríguez de Rivera Manuel Rodríguez de Rivera The accuracy of the direct and local measurements of the heat power dissipated by the surface of the human body, using a calorimetry minisensor, is directly related to the calibration rigor of the sensor and the correct interpretation of the experimental results. For this, it is necessary to know the characteristics of the body’s local heat dissipation. When the sensor is placed on the surface of the human body, the body reacts until a steady state is reached. We propose a mathematical model that represents the rate of heat flow at a given location on the surface of a human body by the sum of a series of exponentials: W(t) = A0 + ∑Aiexp(−t/τi). In this way, transient and steady states of heat dissipation can be interpreted. This hypothesis has been tested by simulating the operation of the sensor. At the steady state, the power detected in the measurement area (4 cm2) varies depending on the sensor’s thermostat temperature, as well as the physical state of the subject. For instance, for a thermostat temperature of 24 °C, this power can vary between 100–250 mW in a healthy adult. In the transient state, two exponentials are sufficient to represent this dissipation, with 3 and 70 s being the mean values of its time constants.

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.