En este documento se demuestra de forma práctica como las aplicaciones móviles pueden ser consideradas como una gran herramienta, en la enseñanza de diversas áreas en básica primaria como son: matemáticas, inglés, sociales, español, entre otras. Es importante anotar cómo los docentes pueden apoyar sus procesos de enseñanza, a través del uso de dispositivos móviles, en los cuales se instalan aplicaciones sencillas de usar para los estudiantes, de fácil entendimiento y manejo. Particularmente aplicaciones como SAIA v1.0, GROBOT v1.0, STEAM MATH v1.0 y STUDENT STEAM v 1.0 que han sido diseñadas a la medida y puestas a disposición de instituciones de básica primaria, con la finalidad de apoyar sus procesos, obteniendo excelentes resultados, representando para ellos herramientas prácticas, de liviana portabilidad y de fácil manipulación, pero sobre todo son aplicaciones que permiten afianzar los conocimientos adquiridos sin la presencia del docente. Educational mobile applications have gained strength in the teaching and learning processes, for this reason, the objective of this paper is to demonstrate how these mobile applications can be considered a great tool in teaching using steam methodology. The paper has a descriptive character, considering the way in which the different forms of learning are supported by mobile devices and are becoming new teaching models. The topics addressed, the services received, the content exposed, among others, are analyzed. The results show a positive impact after the use of the applications on the performance, motivation, attitude and interest in learning of the students in the topics developed, as well as the satisfaction of the teachers for the support of the mobile applications in their work, and how the applications allow reinforcing the knowledge acquired without the presence of the teacher.

In the development of intelligent cities, the automation of vehicular mobility is one of the strong points of research, where intelligent traffic lights stand out. It is essential in this field to prioritize emergency vehicles that can help save lives, where every second counts in favor of the transfer of a patient or injured person. This paper presents an artificial intelligence algorithm based on two stages, one is the recognition of emergency vehicles through a ResNet-50 and the other is a fuzzy inference system for timing control of a traffic light, both lead to an intelligent traffic light. An application of traffic light vehicular flow control for automatic preemption when detecting emergency vehicles, specifically ambulances, is oriented. The training parameters of the network, which achieves 100% accuracy with confidence levels between 65% with vehicle occlusion and 99% in direct view, are presented. The traffic light cycles are able to extend the green time of the traffic light with almost 50% in favor of the road that must yield the priority, in relation to not using the fuzzy inference system.

This paper compares well-known convolutional neural networks (CNN) models for facial recognition. For this, it uses its database created from two registered users and an additional category of unknown persons. Eight different base models of convolutional architectures were compared by transfer of learning, and two additional proposed models called shallow CNN and shallow directed acyclic graph with CNN (DAG-CNN), which are architectures with little depth (six convolution layers). Within the tests with the database, the best results were obtained by the GoogLeNet and ResNet-101 models, managing to classify 100% of the images, even without confusing people outside the two users. However, in an additional real-time test, in which one of the users had his style changed, the models that showed the greatest robustness in this situation were the Inception and the ResNet-101, being able to maintain constant recognition. This demonstrated that the networks of greater depth manage to learn more detailed features of the users' faces, unlike those of shallower ones; their learning of features is more generalized. Declare the full term of an abbreviation/acronym when it is mentioned for the first time.

El presente artículo expone el diseño de un efector robótico construido con sensórica interna en material flexible. Basados en el agarre bioinspirado de objetos delgados típicamente no rígidos realizados con dos dedos por os humanos, se establecen las características del modelo 3 que sirve de base para la impresión de este, incluyendo espacio interno para una flexoresistencia que permita identificar el porcentaje de flexión para agarre, mediante el efector. Se diseña un controlador difuso para control del efector y dada la tolerancia del sensor se emplea un sistema de inferencia difusa Mamdani tipo-2. Los resultados muestran un agarre adecuado que permite obtener un error de estado estacionario cercano a cero, permitiendo agarrar objetos delgados como un pañuelo o una pieza de papel higiénico.

This article designs a high-efficiency electric propulsion system for industrial trucks, such as dumper trucks. This design proposes using an alternative energy storage system of green H2 hydrogen to reduce emissions. This design determines the propulsion systems' technical and power requirements, starting with each vehicle's driving and duty cycles. For this analysis, a longitudinal dynamic model is created, with which the behavior of the energy conversion chain of the propulsion system is established. The evolutionary methodology analyzes the dynamic forces of vehicle interaction to size the propulsion system's components and the storage system. Using green H2 as fuel allows an energy yield three times higher than diesel. In addition, using this green hydrogen prevents the emission of 264,172 kg of CO₂, which the dumper emits when consuming 1,000 daily gallons of diesel within its working day.

This paper shows the development of a virtual environment for a mobile robotic system with the ability to recognize basic voice commands, which are oriented to the recognition of a valid command of bring or take an object from a specific destination in residential spaces. The recognition of the voice command and the objects with which the robot will assist the user, is performed by a machine vision system based on the capture of the scene, where the robot is located. In relation to each captured image, a convolutional network based on regions is used with transfer learning, to identify the objects of interest. For human-robot interaction through voice, a convolutional neural network (CNN) of 6 convolution layers is used, oriented to recognize the commands to carry and bring specific objects inside the residential virtual environment. The use of convolutional networks allowed the adequate recognition of words and objects, which by means of the associated robot kinematics give rise to the execution of carry/bring commands, obtaining a navigation algorithm that operates successfully, where the manipulation of the objects exceeded 90%. Allowing the robot to move in the virtual environment even with the obstruction of objects in the navigation path.<