José Daniel Corona Flores

The rapid growth of sustainable finance has increased the need for transparent and robust decision-support tools for screening environmental, social, and governance (ESG) investment assets. Conventional ESG evaluation methods often rely on composite scores that obscure trade-offs among criteria and inadequately address data imprecision and heterogeneity. This paper proposes a hierarchical interval outranking approach for classifying sustainable finance assets into acceptability categories, rather than producing a full ranking. The method structures ESG criteria hierarchically and represents performance evaluations using intervals to explicitly account for uncertainty, variability, and incomplete information commonly observed in ESG datasets. An outranking-based sorting procedure is then applied to assign assets to predefined classes, such as non-acceptable, acceptable, and priority investments. The framework is demonstrated using publicly available ESG indicators for countries, interpreted as sovereign investment assets, illustrating its ability to deliver transparent, non-compensatory classifications while maintaining interpretability at each aggregation level. The results show that the approach supports consistent and explainable ESG screening decisions, offering a viable alternative to opaque scoring-based methods. The proposed model contributes to sustainable finance research by integrating hierarchical multicriteria decision analysis with interval-based uncertainty handling and provides practical value for portfolio managers and policymakers engaged in responsible investment decision-making.

Student dropout is a complex phenomenon driven by multiple, heterogeneous, and often interacting factors related to academic performance, socioeconomic conditions, engagement, and institutional context. While predictive machine learning models have been widely used to address this problem, they frequently lack transparency and decision-support capabilities required by educational policymakers. In this paper, we propose a hierarchical interval outranking approach to classify students according to their dropout risk, explicitly accounting for imprecision, interaction among criteria, and hierarchical structuring of decision factors. The proposed approach reformulates student dropout assessment as a multi-criteria ordinal classification problem, where students are assigned to ordered risk categories (e.g., high, medium, and low dropout risk). Criteria are organized in a hierarchy reflecting major dimensions of academic success, and criterion performances are modeled using interval data to capture uncertainty and variability inherent in educational records. Interaction effects such as synergy, redundancy, and antagonism between criteria are explicitly incorporated within the outranking framework. The classification is performed using hierarchical extensions of interval-based outranking methods, ensuring consistency, monotonicity, and interpretability of the results. The methodology is illustrated using a real-world dataset on student academic performance and dropout outcomes. Results show that the proposed approach provides interpretable and robust classifications, enabling partial analyses at different levels of the hierarchy and offering meaningful insights into the drivers of dropout risk. Compared to purely predictive approaches, the proposed method enhances transparency and supports informed decision-making for early intervention and academic policy design.

In higher education institutions, the selection of senior administrators such as campus directors is a critical decision-making process that impacts institutional leadership, planning, and academic performance. Despite its importance, the process is typically conducted without formal decision support tools, considering the multidimensional nature of leadership competencies. This work proposes the development of a multicriteria decision aid (MCDA) model for a transparent and methodical director selection. The model employs a mixed-methods design, utilizing qualitative interviews and quantitative questionnaire outcomes to identify and formalize relevant decision criteria. It applies the Value-Focused Thinking approach to organize strategic, fundamental, and operational objectives, alongside the hierarchical interval outranking method to approximate decision-maker preferences under uncertainty and create an ordinal classification of candidates. This approach enables the management of complex evaluation schemes, including hierarchically structured and interacting criteria. The application of the model in a case study involving more than 120 national campuses across Mexico demonstrates that the proposal facilitates better-informed, consistent, and transparent selections compared to typical ad-hoc methods.

For any food, it is important to know consumption, preference, and the characteristics as quality parameters that are important to consumers of a product. The descriptive methodologies are an important tool to know the quality attributes of the products. Within these methodologies is the flash profile (FP), which is based on the generation of the distinctive attributes of the products without any expensive and time-consuming training sessions. The aim of this research was to study the consumption and preference of flour tortillas by consumers and to develop the descriptive characterization of the tortillas by using the flash profile method. The wheat flour tortillas used were two commercial and two handcrafted samples. Ten experienced panelists participated as the FP panel. The panelists generated 22 descriptors, six for texture, seven for appearance, five for odor, and four for flavor. These descriptors differentiate the samples of the flour tortillas. The panelists’ performance was assessed using the consensus index (Rc = 0.508). The first two dimensions of the Generalized Procrustes Analysis represent 83.78% of the data variability. Flash profile proved to be an easy and rapid technique that allowed the distinctive attributes of flour tortillas to be obtained.

The application of coatings with essential oils for food preservation is an alternative way to keep minimally processed apple slices fresh, nutritious, safe, sensory palatable, and accessible for consumers. In the present study, the effect of three bioactive coatings on quality variables of minimally processed Golden Delicious apple slices for 25-days at 4 °C was evaluated. The coatings were CT1-chitosan-based, CT2-guar gum-based, and CT3-composite guar gum-starch-based; all three coatings contained cinnamon essential oil and were compared with UCT0-uncoated apple slices. The quality variables evaluated were weight-loss, firmness, browning index, total phenolic content, total soluble solids, titratable acidity, respiration rate, microbial analysis, and sensory evaluation. All coatings improved the preservation and sensorial quality variables of Golden Delicious apples; however, although the CT1-chitosan-based coating was capable of extending the shelf-life of minimally processed apple, it demonstrated less sensorially favorable scores for flavor, odor, and overall acceptance attributes.

Edible coatings are safe, legal, and sensory acceptable for food applications and they can be incorporated as natural additives due to their antimicrobial activity, thickening capacity, nutrient content, and bioactive agents for protecting seafood from physical, chemical, and microbiological damage that affects its shelf-life. This study aimed to evaluate the effect of the guar gum bioactive coating with thyme oil on the quality of tilapia fish fillets for 15 days of storage at 4 °C, as a means to extend shelf-life. pH, moisture, ash, fat, color, thiobarbituric acid reactive substances (TBARS), total volatile basic nitrogen (TVB-N), microbiological, and sensory examinations were investigated, and the results were analyzed by analysis of variance. The treatments were control (uncoated, UC), GGC (coated with guar gum, GGC), and guar gum combined with thyme oil (GGCTH). Tilapia fillets were stored at 4 °C, the safe temperature for refrigerated storage for 15 days. GGCTH had a slower increase of pH after 15 days of storage in comparison with GGC and UC (p < 0.05). GGC and GGCTH resulted in lower and lowest lightness (L*; p < 0.05) values, lower and lowest redness (a*; p < 0.01) values, and greater and greatest yellowness (b*; p < 0.05) values compared to UC, respectively. UC reduced shear force at 5 (0.37 kgf), 10 (0.32 kgf), and 15 (0.30 kgf) days post-storage in comparison with GGC (0.43, 0.43, and 0.43 kgf) and GGCTH (0.43, 0.44, and 0.44 kgf), respectively. There was less (p < 0.05) deterioration, as well as differences in textural and sensorial variables between uncoated and coated fish fillets. The microbiological analyses demonstrated that there was greater microbial growth in the uncoated fillets than in the coated ones. It was concluded that this bioactive coating with thyme oil retards microbial colonization of fish and reduces degradability of quality variables, therefore, it is a reliable and effective alternative to extend the shelf-life of tilapia fillets.

Objetivo: identificar el grado de pertinencia del Programa Docentede Ingeniero en Ciencia y Tecnología de Alimentos (PDICTA) para enfrentar los retos que demanda la Cuarta Revolución Industrial o Industria 4.0. Método: para la recolección de los datos se aplicó una encuesta como instrumento de medición, utilizando la escala de Likert de cinco opciones de respuesta a 10 ítemspara una muestra representativa de 12 profesores y 14 egresados del programa. Para el análisis de los datos de la encuesta, se aplicó la estadística descriptiva. Resultados:Los hallazgos fueron los siguientes: en 4 de los items, los profesores y egresados manifestaron estar de acuerdo, mientras que en los 6 items restantes, los resultados mostraron diferentes respuestas que estuvieron en niveles que variaron desde estar en desacuerdo, neutral y de acuerdo.Discusión y conclusión:Tanto los profesores como los egresados, estuvieron de acuerdo en que los primeros apoyaron a los segundos en el desarrollo de cuatro de las diez habilidades o competencias que demanda la Industria 4.0, lo que indica queel PDICTA está en un proceso de cambio para incurcionsr en el mercado laboral que demanda ia Industria 4.0.

Factores que influyen en la implementación de la educación a distancia