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: 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: 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.

Description: Globally, current food consumption and trade are placing unprecedented demand on agricultural systems and increasing pressure on natural resources, requiring tradeoffs between food security and environmental impacts especially given the tension between market-driven agriculture and agro-ecological goals. In order to illustrate the wicked social, economic and environmental challenges and processes to find transformative solutions, we focus on the largest concentration of greenhouses in the world located in the semi-arid coastal plain of South-east Spain. Almería family farming, predominantly cooperative, greenhouse intensive production, commenced after the 1960s and has resulted in very significant social and economic benefits for the region, while also having important negative environmental and biodiversity impacts, as well as creating new social challenges. The system currently finds itself in a crisis of diminishing economic benefits and increasing environmental and social dilemmas. Here, we present the outcomes of multi-actor, transdisciplinary research to review and provide collective insights for solutions-oriented research on the sustainability of Almeria’s agricultural sector. The multi-actor, transdisciplinary process implemented collectively, and supported by scientific literature, identified six fundamental challenges to transitioning to an agricultural model that aims to ameliorate risks and avoid a systemic collapse, whilst balancing a concern for profitability with sustainability: (1) Governance based on a culture of shared responsibility for sustainability, (2) Sustainable and efficient use of water, (3) Biodiversity conservation, (4) Implementing a circular economy plan, (5) Technology and knowledge transfer, and (6) Image and identity. We conclude that the multi-actor transdisciplinary approach successfully facilitated the creation of a culture of shared responsibility among public, private, academic, and civil society actors. Notwithstanding plural values, challenges and solutions identified by consensus point to a nascent acknowledgement of the strategic necessity to locate agricultural economic activity within social and environmental spheres.This paper demonstrates the need to establish transdisciplinary multi-actor work-schemes to continue collaboration and research for the transition to an agro-ecological model as a means to remain competitive and to create value.

Description: Feature selection is a primary exercise to tackle any forecasting task. Machine learning algorithms used to predict any variable can improve their performance by lessening their computational effort with a proper dataset. Anticipating future glycemia in type 1 diabetes mellitus (DM1) patients provides a baseline in its management, and in this task, we need to carefully select data, especially now, when novel wearable devices offer more and more information. In this paper, a complete characterization of 25 diabetic people has been carried out, registering innovative variables like sleep, schedule, or heart rate in addition to other well-known ones like insulin, meal, and exercise. With this ground-breaking data compilation, we present a study of these features using the Sequential Input Selection Algorithm (SISAL), which is specially prepared for time series data. The results rank features according to their importance, regarding their relevance in blood glucose level prediction as well as indicating the most influential past values to be taken into account and distinguishing features with person-dependent behavior from others with a common performance in any patient. These ideas can be used as strategies to select data for predicting glycemia depending on the availability of computational power, required speed, or required accuracy. In conclusion, this paper tries to analyze if there exists symmetry among the different features that can affect blood glucose levels, that is, if their behavior is symmetric in terms of influence in glycemia.

Description: Purpose: Investigations suggest non-medical use of prescription drugs (NMUPD) is associated with heavy drinking and polydrug use among university students. Our aim is to determine the prevalence of NMUPD among university students and to analyze its association with alcohol, tobacco, and cannabis use, and to study the role of the age of drinking onset. Methods: Cohort study among university Spanish students (n = 1382). Heavy drinking (HED) and risky consumption (RC) were measured with the Alcohol Use Disorders Identification Test. Questions related to tobacco and cannabis consumption were also formulated. NMUPD refers to sedative, anxiety, or pain medication intake within the last 15 days without medical prescription. All variables were measured at 18, 20, and 27 years. Multilevel logistic regression for repeated measures was used to obtain adjusted OR (odds ratios). We analyzed the results from a gender perspective. Results: Prevalence of NMUPD were higher in students who already partook in NMUPD at the beginning of the study. NMUPD in women at 27 is 3 times higher than at 18, while in men it is twice. Among females, RC (OR = 1.43) and cannabis consumption (OR = 1.33) are risk factors for NMUPD, while later onset of alcohol use (OR = 0.66) constitutes a protective factor. No significant differences were found for males. Conclusions: NMUPD is prevalent among university students. RC and early onset of alcohol use were associated with higher prevalence of NMUPD in females. The prevalence of NMUPD increased with age in both sexes. Strategies for reducing risky drinking and delaying onset of drinking should be provided for university students. Pharmacists and parents should be alerted to the risk of NMUPD.

Description: Objective: To compare the differences in the modes and distance of the displacements in high school and university stage in the same sample. Methods: A total of 1288 volunteer university students (614 males and 674 females) participated, with an average age of 22.7 ± 5.8 years, belonging to four private and public universities in Chile where a validated self-report questionnaire was applied to the study, which included the modes, travel time, and distance at school and university. Results: The active commuting decreases from school to university when leaving home (males: 39.6% to 34.0%; p = 0.033 and females: 32.9% to 18.5%, p 〈 0.001), as well as when returning (males: 44.1% to 33.7%; p 〈 0.001 and females: 38.6% to 17.6%, p 〈 0.001). Conversely, non-active modes of transport increase, especially in females (go: 67.1% to 81.4%, return: 61.5% to 82.6%), affected by the increase in the use of public transportation in university. It was also defined that at both school and at university, the active commuting decreases the greater the distance travelled. Conclusion: The active modes of commuting decreased between high school and university and the non-active mode of commuting was the most frequent form of mobility to high school and university, observing that the active trips decreased when the distance from the home to high school or university increased. Public and private intervention policies and strategies are required to maintain or increase the modes of active commuting in the university stage for an active life in adulthood.

Description: The type of hospital (public or private) has been associated with the type of clinical practice carried out. The purpose of this study was to determine the association between the type of hospital (public or private) and delivery attendance with practices based on the recommendations by the World Health Organization (WHO). A cross-sectional study with puerperal women (n = 2906) was conducted in Spain during 2017. The crude Odds Ratios (OR), adjusted (aOR) and their 95% confidence intervals (CI) were calculated through binary logistic regression. For multiparous women in private centers, a higher rate of induced labor was observed (aOR: 1.49; 95% CI: 1.11–2.00), fewer natural methods were used to relieve pain (aOR: 0.51; 95% CI: 0.35–0.73), and increased odds of cesarean section (aOR: 2.50; 95% CI: 1.81–3.46) were found as compared to public hospitals. For primiparous women in private centers, a greater use of the epidural was observed (aOR: 1.57; 95% CI: 1.03–1.40), as well as an increased likelihood of instrumental birth (aOR: 1.53; 95% CI: 1.09–2.15) and of cesarean section (aOR: 1.77; 95% CI: 1.33–2.37) than in public hospitals. No differences were found in hospitalization times among women giving birth in public and private centers (p 〉 0.05). The World Health Organization birth attendance recommendations are more strictly followed in public hospitals than in private settings.