Teaching-learning processes have been affected by the changes that the introduction of information and communication technologies are making to the current global dynamics. This study examines the trends and evolution of the application of virtual tools in teaching-learning processes. Using a bibliometric analysis, 104 articles retrieved using a search equation defined under the PRISMA methodology were analysed. The results allowed the identification of the most influential contributions, authors, and journals, as well as the trends of research carried out in the field, identifying the authors García-Peñalvo, Sánchez-Prieto and Olmos-Migueláñez as the main references in terms of productivity and impact, as well as the journal Computers in Human Behaviour as the most important in academic impact; additionally, it is identified that COVID-19 and online education are among the main emerging concepts, and higher education and the TAM are among the most solid in the research field. Similarly, the research carried out can be classified into four categories based on their main topic: the acceptance of technology; the design of instruments; the design of platforms; and relevant content. Among the main conclusions, it is mentioned that these tools provide aspects of flexibility, coverage and accessibility at all educational levels.

This research investigates the effectiveness of four metaheuristic algorithms, the Population-Based Genetic Algorithm, Particle Swarm Optimization, JAYA, and Generalized Normal Distribution Optimizer, for managing the energy production of wind-based distributed generators (DGs). The aim is to reduce operational costs in a 33-node microgrid (MG) operating under both connected and isolated configurations. The study seeks to identify the most efficient algorithm for minimizing operational expenses in distributed generation systems, specifically in terms of energy production and purchasing costs, as well as the maintenance costs of DGs. Due to limited statistical validation and unrealistic operational constraints in previous studies, we propose a novel framework that offers a robust, reproducible solution for optimizing the management of wind-based distributed generators in microgrids. Through 100 independent trials for each algorithm and configuration, rigorous statistical analyses are conducted, including ANOVA and Tukey’s post hoc test, to assess performance consistency and the significance of cost reduction outcomes across algorithms. The results indicate that the PGA demonstrates superior cost efficiency and stability, particularly in the connected MG configuration.

Traditional optimization approaches for power transmission systems often optimize gear and shaft designs separately, rely on conventional materials, and face challenges handling mixed-variable problems involving discrete and continuous variables. These limitations hinder the potential for significant weight reduction and performance improvements in modern mechanical systems. This research presents an innovative optimization approach for reducing the gear pitch in a power transmission system comprising a shaft with two gears. By employing bio-inspired metaheuristic algorithms and utilizing materials common in additive manufacturing and composite materials, the study aims to minimize system weight while ensuring mechanical integrity. The optimization integrates gear and shaft design variables, accurately models mechanical constraints through penalty functions, and leverages the unique properties of advanced materials to enhance performance. In this work, we developed an integrated optimization model that overcomes the limitations of previous studies by combining advanced materials with bio-inspired algorithms to achieve significant weight reduction in gear systems. This approach effectively addresses the complexities of mixed-variable optimization and mechanical constraints, providing a practical and innovative solution for modern engineering applications.

In this paper, we propose a model to assess the adoption of e-learning tools by professors in the context of COVID-19. The proposed model was constructed by combining three technology adoption models: the Theory of Reasoned Action, the Theory of Planned Behavior, and the Technology Acceptance Model. This study followed a quantitative methodology based on the collection of data from 123 questionnaires administered to professors from different higher education institutions in Medellín (Colombia). According to the results, the variables that most influence the adoption of e-learning tools by professors are compatibility, self-efficacy, previous experience, satisfaction, infrastructure, administrative intervention, social influence, and technical support.

The inverse kinematics (IK) problem of the UR3 collaborative manipulator is addressed through a singularity-aware optimization framework and a statistically grounded benchmarking methodology. The IK task is formulated as a full-pose optimization problem minimizing a physically scaled residual combining Cartesian position and orientation errors. Emphasizing consistency between error formulation and optimization paradigms, a matrix-based pose-error representation is adopted as a numerically stable residual for stochastic search. Simultaneously, a smooth Jacobian-conditioning penalty is incorporated to mitigate instability near ill-conditioned configurations. Five metaheuristic solvers (PSO, GWO, GA, JADE, ALO) are implemented under a unified, reproducible experimental protocol with common maximum search settings. The Levenberg–Marquardt (LM) numerical method is included as a deterministic baseline to compare gradient-based precision against derivative-free global exploration. Performance is evaluated across nominal, industrial, and near-singular poses using 1000 Monte Carlo runs per configuration. Final-solution accuracy, variability, and computational time are analyzed directly from the Monte Carlo outcome distributions, descriptive statistics, and nonparametric rank-based tests. Results indicate that LM achieves superior numerical precision and computational speed. Among the metaheuristics, GA provides the lowest mean objective values and the smallest objective dispersion across the three tested poses, whereas JADE is the fastest solver. GWO provides an intermediate solution profile, with competitive objective values and substantially shorter execution times than GA and ALO. The optimized solutions are first verified in a RoboDK virtual environment. Subsequently, representative GWO-based configurations are experimentally validated on a physical UR3 robot through both isolated static poses and a continuous multi-pose trajectory tracking task, confirming practical kinematic feasibility and sequential stability. The proposed framework establishes a reproducible benchmark for statistically robust evaluation of metaheuristic-based IK optimization in collaborative robotics.

Urban service robots require route planners that are not only collision-free but also consistent with the energetic behavior of differential drive locomotion. Conventional grid planners such as A* are efficient and reliable for geometric navigation, but their usual cost structure prioritizes path length and does not explicitly account for heading changes, concentrated turns, or localization risk near obstacles. This study presents an energy-aware route-planning formulation for differential-drive mobile robots operating in dense polygonal urban environments. The path is encoded through twelve continuous internal waypoints and is evaluated using an interpretable energy proxy that combines translational distance, cumulative absolute rotation, squared rotation, and a clearance-dependent localization risk term. Collision avoidance, boundary compliance, and maximum turn feasibility are handled through a feasibility-first dominance rule, and the resulting constrained problem is solved using a Genetic Algorithm and Particle Swarm Optimization. A* with clearance inflated occupancy grids is included as a deterministic baseline. The final experiments used a dense urban scenario with sixteen polygonal obstacles, an A* grid resolution of 0.10 m, a robot radius of 0.20 m, a safety clearance of 0.10 m, 80 GA individuals, 80 PSO particles, 100 iterations, and 10 independent runs. All methods achieved a 100% feasibility rate. PSO obtained the lowest average energy proxy, 18.505, compared with 18.645 for A* and 18.645 for GA, and reduced average rotation from 4.136 rad to 4.079 rad. However, A* remained much faster, 5.860 s on average, compared with 174.574 s for GA and 175.869 s for PSO. The ranking analysis shows that GA produced the best aggregate score when route quality, time, clearance, and feasibility were weighted equally, while PSO produced the best route quality. External perturbation tests indicate that open-loop execution in narrow corridors is sensitive to bias and waypoint noise, which motivates closed-loop tracking, online replanning, and physical validation in future work.