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.

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.

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 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.

Agricultural product prices are subject to various uncertainties, including unpredictable weather conditions, pest infestations, and market fluctuations, which can significantly impact agricultural yields and productivity. Accurately assessing and understanding price is crucial for farmers, policymakers, and stakeholders in the agricultural sector to make informed decisions and implement appropriate risk management strategies. This study used the ordered weighted average (OWA) operator and its extensions as mathematical aggregation techniques incorporating ordered weights to capture and evaluate the factors influencing price variation. By generating different vectors related to different inputs to the traditional formulation, it is possible to aggregate information to calculate and provide a new view of the outcomes. The results of this research can help enhance risk management practices in agriculture and support decision-making processes to mitigate the adverse effects of price.

The economic impact of COVID-19 is undeniable, and one of the sectors most affected by this situation was tourism; when departures were canceled and what is known as “The Great Lockdown” began, the activity of this sector was paralyzed. In this regard, knowing which strategic actions must be implemented in order to recover economically is vital. This study aims to identify the importance of the strategic actions of travel agencies in Boyacá following the COVID-19 crisis using aggregation operators and fuzzy techniques. The methodology uses the experton method, Bonferroni’s OWAAC method, maximum similarity sub-relationships and Pichat’s algorithm, and the relative incidence analysis method to determine the importance of the actions taken. The findings show that most managers’ implemented strategic actions, including highlighting financial capacity and marketing (improvement actions and establishing alliances), which were the strategic actions with the highest incidence. These actions identify a focus for activities to reactivate the business and are related to the company’s routine operations.

This paper presents a new aggregation operator technique that uses the ordered weighted average (OWA), heavy aggregation operators, Hamming distance, and moving averages. This approach is called heavy ordered weighted moving average distance (HOWMAD). The main advantage of this operator is that it can use the characteristics of the HOWMA operator to under-or overestimate the results according to the expectations and the knowledge of the future scenarios, analyze the historical data of the moving average, and compare the different alternatives with the ideal results of the distance measures. Some of the main families and specific cases using generalized and quasi-arithmetic means are presented, such as the generalized heavy moving average distance and a generalized HOWMAD. This study develops an application of this operator in forecasting the sales growth rate for a commercial company. We find that it is possible to determine whether the company's objectives can be achieved or must be reevaluated in response to the actual situation and future expectations of the enterprise.

One of the problems investors often face is deciding on the stocks to include in an investment portfolio. Hence, this article seeks to select investment portfolios considering the 30 leading companies listed on the New York Stock Exchange (NYSE) of the Dow Jones Index. Portfolio selection in this index is carried out by generating a previous ranking of the shares with a novel approach that analyzes their performance using a multiple criteria hierarchical process (MCHP). The current research allows the evaluation of shares and optimizing a portfolio, while developing a procedure that supports a decision-making process for organizations or practitioners to invest in the stock market. The results of analyzing the NYSE generated a portfolio that can be used for interested investors. The main contribution of the application of MCHP and Markowitz model is related to the possibility of replicating the developed procedure to select portfolios in other stock markets.