This paper presents a set of adaptive parameter control methods through reinforcement learning for the particle swarm algorithm. The aim is to adjust the algorithm’s parameters during the run, to provide the metaheuristics with the ability to learn and adapt dynamically to the problem and its context. The proposal integrates Q–Learning into the optimization algorithm for parameter control. The applied strategies include a shared Q–table, separate tables per parameter, and flexible state representation. The study was evaluated through various instances of the multidimensional knapsack problem belonging to the $\mathcal{NP}$-hard class. It can be formulated as a mathematical combinatorial problem involving a set of items with multiple attributes or dimensions, aiming to maximize the total value or utility while respecting constraints on the total capacity or available resources. Experimental and statistical tests were carried out to compare the results obtained by each of these hybridizations, concluding that they can significantly improve the quality of the solutions found compared to the native version of the algorithm.

Background and objective: During adolescence, physical fitness attributes are a key factor positively influencing not only physical health, but also cognitive, psychological and social development. The objective was to derive data-driven physical fitness profiles and to characterize their sex-specific structure in Chilean adolescents using unsupervised machine learning and an explainable artificial intelligence (AI) framework. Methods: This cross-sectional study included a nationally representative sample of 25,912 Chilean schoolchildren (14,184 boys and 11,728 girls) aged 13 to 17 years. Six unsupervised clustering algorithms were applied separately by sex, each configured to partition participants into three clusters representing low, medium, and high physical performance levels. Results: K-means achieved the highest composite internal validity score for both sexes (boys: 0.959, Calinski–Harabasz 5757.7, Davies–Bouldin 1.203; girls: 0.976, Calinski–Harabasz 4156.1, Davies–Bouldin 1.349), and the partition was highly stable across 500 age-stratified bootstrap resamples (adjusted Rand index 0.973 in boys and 0.967 in girls). A Random-Forest surrogate reproduced the cluster labels with high fidelity (macro-averaged F1 0.984 in both sexes; Cohen’s κ 0.973 in boys and 0.975 in girls). Conclusions: Unsupervised machine learning with explainable AI provides a robust, transparent, sex-specific framework for stratifying adolescent fitness; cardiorespiratory capacity and abdominal endurance are the components that most consistently distinguish the profiles, while lower-limb power and upper-body endurance differentiate the higher performance tiers.

Background: This study focuses on the quantitative analysis of football players’ performance based on a set of performance data. Objective: The objective of this study was to develop a multivariate analytical model to identify, quantify, and predict the most relevant factors that determine the offensive and defensive profiles of professional football players, thereby optimizing functional segmentation, performance analysis, and decision-making processes in the contexts of scouting and data-based tactical planning. Materials and methods: A dataset was compiled through automated extraction (web scraping) via the Python programming language and its specialized libraries. The full dataset comprised 2,689 player–league records corresponding to 2,530 unique players from Europe’s five major UEFA-affiliated national leagues during the 2022–2023 season (159 players who transferred between two leagues mid-season contributed one record per league, 2,530 + 159 = 2,689). After applying a minimum-eligibility filter of >450 minutes played to ensure stable per-90 metrics, 1,624 player–league records (1,620 unique players) were retained for the modeling workflow. Dimensionality reduction techniques (PCA) and explainable machine learning (Shapley Additive exPlanations [SHAP]) with predictive modeling were applied. Eight algorithms were trained and compared to predict PC1 Offensive and PC1 Defensive. Results: The First Offensive Principal Component (PC1 Offensive) synthesizes actions that generate scoring opportunities, such as actions prior to shots (SCA_p90, 13.3%), progressive receptions (RecProg_p90, 13.0%) and total shots (Shots_p90, 12.7%). The first-defensive principal component (PC1 Defensive) highlights tackles (Tkl_p90, 19.1%), blocks (15.6%) and interceptions (15.6%). Conclusions: Finally, the inclusion of SHAP allows for an in-depth interpretation of the models, identifying the key statistical factors that influence players’ offensive and defensive contributions, which is valuable for scouting, tactical analysis and player development.

La industria minera, fundamental para la economía global por su extracción de recursos naturales y suministro de materias primas esenciales para diversas industrias, enfrenta desafíos continuos en eficiencia, productividad y sostenibilidad. Aunque se han implementado avances tecnológicos significativos, incluyendo automatización, inteligencia artificial y aprendizaje automático, la integración de metodologías ágiles en los procesos de extracción minera permanece inexplorada. Este artículo presenta un enfoque que combina la metodología SCRUM con tecnología avanzada de inteligencia artificial para optimizar la gestión de extracción de material en operaciones mineras. Proponemos un modelo donde cada elemento del proceso de extracción se convierte en parte del equipo SCRUM, con un Modelo de Lenguaje Grande (LLM) actuando como SCRUM Master y un Ingeniero de Operaciones Mineras como Product Owner. Detallamos la estructura del equipo, los procesos adaptados de SCRUM, la implementación tecnológica necesaria y el flujo de trabajo diario.

Signed networks are graphs with binary weights on their edges, used to represent polarized opinion relationships between actors. They are widely applied to study clusterability and structural balance in social structures. This article proposes using large language models (LLMs) to automatically generate temporal signed networks from textual discussions. To enhance explainability, each signed relationship is linked to specific excerpts from interactions conveying non-neutral polarity (positive or negative). As a case study, the article examines talk pages about controversial topics on Spanish Wikipedia. The results show that current LLMs can effectively extract the core of peer disputes, yielding traceable and explainable representations of discursive conflict situations.