Dr. León-Castro, Ernesto

The increasing development of gadgets to evaluate stress, wellness, and anxiety has garnered significant attention in recent years. These technological advancements aim to expedite the identification and subsequent treatment of these prevalent conditions. This study endeavors to critically examine the latest smart gadgets and portable techniques utilized for diagnosing depression, stress, and emotional trauma while also exploring the underlying biochemical processes associated with their identification. Integrating various detectors within smartphones and smart bands enables continuous monitoring and recording of user activities. Given their widespread use, smartphones, smartwatches, and smart wristbands have become indispensable in our daily lives, prompting the exploration of their potential in stress detection and prevention. When individuals experience stress, their nervous system responds by releasing stress hormones, which can be easily identified and quantified by smartphones and smart bands. The study in this paper focused on the examination of anxiety and stress and consistently employed “heart rate variability” (HRV) characteristics for diagnostic purposes, with superior outcomes observed when HRV was combined with “electroencephalogram” (EEG) analysis. Recent research indicates that electrodermal activity (EDA) demonstrates remarkable precision in identifying anxiety. Comparisons with HRV, EDA, and breathing rate reveal that the mean heart rate employed by several commercial wearable products is less accurate in identifying anxiety and stress. This comprehensive review article provides an evidence-based evaluation of intelligent gadgets and wearable sensors, highlighting their potential to accurately assess stress, wellness, and anxiety. It also identifies areas for further research and development.

The aim of this paper is to present the application of the OWA operator and some of its extensions in the calculation of continent and global tax revenues. The idea is to present how the analysis of an important economic indicator can vary depending on how the information is aggregated. An example was employed based on the Organization for Economic Co-operation and Development (OECD) database using 111 countries that were divided by continent, and then the global tax revenue was calculated using different aggregation operators. Different analyses can be carried out by governments and enterprises to improve decision making and fiscal politics.

Human capital management is a strategic element for companies in a globalized world. Therefore, they must use strategies and methods to recruit and select personnel assertively to focus their training, strengthening, and business growth efforts. Personnel selection can be seen as a decision problem and can be addressed in a multi-criteria decision-making context. This work aims to present the selection process of a barista in a Mexican coffee shop. The baristas could be the face of the company to customers, and they could significantly impact their overall experience. The personnel selection process included eleven candidates and three criteria. This process was performed using the ELECTRE-III to model the preferences of a decision-maker and RP2-NSGA-II+H, a multi-objective evolutionary algorithm that exploits fuzzy outranking relations to derive multi-criteria rankings. The ordering obtained with the algorithm did not have any inconsistency concerning the integral preference model, and it allowed for the selection of a candidate to occupy the barista position. The results show the relevance of combining preference modeling with multi-criteria analysis methods for decision-making and artificial intelligence techniques.

The paper analyses the strategic planning in the industrial context of Bogota, Colombia during COVID-19. Because of the different perspectives and subjectivity of the topic, the objective is to use an innovative methodology based on different multi-criteria decision-making (MCDM) characteristics and fuzzy logic to analyse the problem. The methodologies used were the Bonferroni ordered weighted average (OWA) operator and the Pitchat algorithm. The study was based on five sectors with seven strategic scopes. Among the main results was possible to visualise that all the sectors find the most important scope in marketing and the less important scope in Formalisation. Finally, some specific analyses of the results for each sector are presented to visualise how the scopes of the strategic planning can change depending on the sector.

Attracting foreign investment is essential for the competitiveness and prosperity of nations. When deciding where to invest, an investor may be interested in considering specific criteria for investing or doing business and preferences for those criteria. In the same way, when evaluating the situation of a country to attract investment, consider different aspects to determine its ease of doing business. The MultiCriteria Decision-Making (MCDM) methodology is suitable for evaluating nations according to their ease of doing business due to the multifactorial elements of each nation. This work applied the ELECTRE-III method; it evaluated 190 nations based on the decision-maker?s preferences, giving different importance to the ten criteria considered in the World Bank?s Doing Business 2020 study. The results with this methodology show better-positioned nations in the ranking compared to the report presented by the World Bank Group.

The main purpose is to propose a new method for approximating the average interest rate using an ordered weighted average. The ordered weighted average (OWA), generalized GOWA operator and interval numbers are studied to develop this method. Using average interest rate formulation combined with aggregations operators and interval numbers presented above is the proposed weighted average interest rate (OWAIR) operator. For mathematical application, the deposit interest rates of the countries with 2020 data on the World Bank website are carried out. Finally, it should be noted that changes in the maximum, minimum, and average values are considered in the use of training for each region, which can be helpful for investors’ decision-making.

The manuscript is a digest, which puts forward findings from previous research papers, combined with new proposals. Approaches comprise two full models' derivation for photovoltaic (PV) systems energy conversion predictability. It brings in several models for key physical observables formulated as functions of the operating conditions. The proposals encompass mean spectral reflectance, coefficient for reflections and spatial geometry, incident angular losses factor, angular losses, and fill factor along with its coefficient of temperature. Applying the superposition principle, these models are integrated into two full approaches for performance predictability. The underlying physics description is mathematically consistent with experimental measurements of the physical observables involved, reported in other studies. To the authors' knowledge, these full models have been reported previously nowhere. Simulation results from the more inaccurate of two full models show good agreement of these findings with the experimental evidence, reported of its performance. The resulting key performance indicators (KPIs), after simulating a grid-connected PV system located in Cuba, yield 1.61%, 13.10%, −1.61%, 2.02%, and 0.81 of MAE, MAPE, MBE, RMSE, and R2, respectively, which they confirm the model's good behavior. Approaches formulations, as functions of solar irradiance and module temperature, its derivations, applications, and model's simulation results are considered the main manuscript novelties.

The main objective of this article is to present the formulation of a Capital Asset Pricing Model ordered weighted average CAPMOWAand its extensions, called CAPM-induced OWA (CAPMIOWA), CAPM Bonferroni OWA (CAPMBon-OWA), and CAPM Bonferroni-induced OWA CAPMBon-IOWA. A step-by-step process for applying this new proposal in a real case of formulating investment portfolios is generated. These methods show several scenarios, considering the attitude, preferences, and relationship of each argument, when underestimation or overestimation of the information by the decision maker may influence the decision-making process regarding portfolio investments. Finally, the complexity of the method and the incorporation of soft information into the modeling process lead to generating a greater number of scenarios and reflect the attitudes and preferences of decision makers.

El objetivo de este trabajo es clasificar las estrategias de marketing digital de una empresa mexicana mediante el operador promedio ponderado ordenado (OWA). La aplicación de esta técnica permite crear un ordenamiento decreciente de las campañas de acuerdo a un vector de ponderación. Entre los hallazgos resalta el hecho de que  es la táctica con los niveles de rendimiento más alto. Por su parte,  resultó ser la opción con la valoración más baja según el desempeño de sus criterios. Sin duda, esta información puede ser una ventaja para los gestores de la mercadotecnia porque le permiten tomar mejores decisiones en función de la asignación de sus recursos ya que, con este instrumento de lógica difusa es posible identificar las áreas de oportunidad, pero también aquellos espacios que se les debe prestar mayor atención. Por último, los resultados también demuestran la aplicación del operador OWA para ordenar los planes de mercadotecnia digital de una empresa de accesorios para celular.