This study explores the implementation of the analytic hierarchy process in usability evaluations, specifically focusing on user interface assessment during software development phases. Addressing the challenge of diverse and unstandardized evaluation methodologies, our research develops and applies a tailored algorithm that simplifies heuristic prioritization. This novel method combines the analytic hierarchy process framework with a bespoke algorithm that leverages transitive properties for efficient pairwise comparisons, significantly reducing the evaluative workload. The algorithm is designed to facilitate the estimation of heuristic relevance regardless of the number of items per heuristic or the item scale, thereby streamlining the evaluation process. Rigorous simulation testing of this tailored algorithm is complemented by its empirical application, where seven usability experts evaluate a web interface. This practical implementation demonstrates our method’s ability to decrease the necessary comparisons and simplify the complexity and workload associated with the traditional prioritization process. Additionally, it improves the accuracy and relevance of the user interface usability heuristic testing results. By prioritizing heuristics based on their importance as determined by the Usability Testing Leader—rather than merely depending on the number of items, scale, or heuristics—our approach ensures that evaluations focus on the most critical usability aspects from the start. The findings from this study highlight the importance of expert-driven evaluations for gaining a thorough understanding of heuristic UI assessment, offering a wider perspective than user-perception-based methods like the questionnaire approach. Our research contributes to advancing UI evaluation methodologies, offering an organized and effective framework for future usability testing endeavors.

The identification of influencing factors in crop yield (kg·ha-1) provides essential information for decision-making processes related to the prediction and improvement of productivity, which gives farmers the opportunity to increase their income. The current study investigates the application of multiple machine learning algorithms for cocoa yield prediction and influencing factors identification. The Support Vector Machines (SVM) and Ensemble Learning Models (Random Forests, Gradient Boosting) are compared with Least Absolute Shrinkage and Selection Operator (LASSO) regression models. The considered predictors were climate conditions, cocoa variety, fertilization level and sun exposition in an experimental crop located in Rionegro, Santander. Results showed that Gradient Boosting is the best prediction alternative with Coefficient of determination (R2) = 68%, Mean Absolute Error (MAE) = 13.32, and Root Mean Square Error (RMSE) = 20.41. The crop yield variability is explained mainly by the radiation one month before harvest, the accumulated rainfall on the harvest month, and the temperature one month before harvest. Likewise, the crop yields are evaluated based on the kind of sun exposure, and it was found that radiation one month before harvest is the most influential factor in shade-grown plants. On the other hand, rainfall and soil moisture are determining variables in sun-grown plants, which is associated with the water requirements. These results suggest a differentiated management for crops depending on the kind of sun exposure to avoid compromising productivity, since there is no significant difference in the yield of both agricultural managements.

This study delves into the realm of heuristic evaluation within usability testing, focusing on refining and applying a tailored algorithm that simplifies the implementation of the Analytic Hierarchy Process for heuristic prioritization. Addressing the prevalent challenge of disparate evaluation methodologies and the absence of a standardized process in usability testing, our research introduces a novel method that leverages the Analytic Hierarchy Process alongside a bespoke algorithm. This algorithm utilizes transitive properties for pairwise comparisons, significantly reducing the evaluative workload. This innovative approach facilitates the estimation of heuristic relevance irrespective of the number of items per heuristic or the item scale and streamlines the overall evaluation process. In addition to rigorous simulation testing of the tailored algorithm, we applied our method in a practical scenario, engaging seven usability experts in evaluating a web interface. This empirical application underscored our method’s capacity to diminish the requisite comparison count and spotlight areas of improvement for critically weighted yet underperforming heuristics. The outcomes of this study underscore the efficacy of our approach in enhancing the efficiency and organization of heuristic evaluations, paving the way for more structured and effective usability testing methodologies in future research endeavors.

The Job Shop Scheduling Problem (JSP) is a combinatorial optimization problem cataloged as type NP-Hard. To solve this problem, several heuristics and metaheuristics have been used. In order to minimize the makespan, we propose a Memetic Algorithm (MA), which combines the exploration of the search space by a Genetic Algorithm (GA), and the exploitation of the solutions using a local search based on the neighborhood structure of Nowicki and Smutnicki. The genetic strategy uses an operation-based representation that allows generating feasible schedules, and a selection probability of the best individuals that are crossed using the JOX operator. The results of the implementation show that the algorithm is competitive with other approaches proposed in the literature.

Introducción: los métodos de aprendizaje automático permiten manejar datos estructurados y no estructurados para construir modelos predictivos y apoyar la toma de decisiones. Objetivo: identificar los métodos de aprendizaje automático aplicados para predecir el comportamiento epidemiológico de enfermedades arbovirales utilizando datos de vigilancia epidemiológica. Metodología: se realizó búsqueda en EMBASE y PubMed, análisis bibliométrico y síntesis de la información. Resultados: se seleccionaron 41 documentos, todos publicados en la última década. La palabra clave más frecuente fue dengue. La mayoría de los autores (88,3 %) participó en un artículo de investigación. Se encontraron16 métodos de aprendizaje automático, el más frecuente fue Red Neuronal Artificial seguido de Máquinas de Vectores de Soporte. Conclusiones: en la última década se incrementó la publicación de trabajos que pretenden predecir el comportamiento epidemiológico de arbovirosis por medio de diversos métodos de aprendizaje automático que incorporan series de tiempo de los casos, variables climatológicas, y otras fuentes de información de datos abiertos.