La bancarización está en apogeo en aras de proporcionar caminos para la reactivación económica postpandemia en América Latina. El objetivo fue analizar la bancarización y políticas de reactivación económica desde publicaciones registradas en la base de datos Scopus bajo los criterios de documentos publicados en países latinoamericanos. Se realizó un análisis bibliométrico, bajo un enfoque sistemático; se utilizó VOSVIEWER para construir redes bibliométricas. Se logró identificar 113 publicaciones, donde Brasil con 37, es el país con mayor producción. Se evidenció una disminución de artículos científicos en 2015, 2017 y 2019 debido a la desconfianza del público y falta de información en revistas científicas. Para los años 2016 y 2020 la cifra se fue incrementando debido al estudio de los bancos de inversión estatal a través de una tipología en 4 roles contra cíclico. Entonces, a causa de la pandemia se obtuvo un crecimiento por la necesidad de la bancarización digital.

The purpose of this work is to create a robot capable of solving the Rubik's Cube. This involves three important stages. The first stage involves detecting the state of the cube, that is, determining how it is mixing by knowing the colors on each face. This involves using artificial vision through a camera and a computer that processes the images. Artificial vision programming is based on the HSV model, where the parameters for detecting colors are: hue, saturation, and value. The second stage consists of obtaining the movements required to solve the cube using the Kociemba algorithm. In the third stage, the movements obtained in the second stage are executed, where the ATmega2560 microcontroller is responsible for rotating each face of the cube using stepper motors. The robot was designed with a configuration that allows it to rotate all six faces of the cube and was built with 3D-printed parts and acrylic sheets. For machine vision, a box was built to scan the cube so that outside lighting wouldn't affect color recognition. The robot finally solved the cube in an average time of six seconds.

This comprehensive research article presents the empirical validation of a strategic maintenance engineering model through the systematic testing of 100 hypotheses (H1-H100) examining the complex interrelationships between external environmental factors, internal operational conditions, mediating results, and final organizational outcomes in physical asset maintenance management. The study employs a mixed-methods approach combining quantitative survey data from 124 organizations with qualitative inter-views, utilizing structural equation modeling and T-Student tests for hypothesis validation. The research investigates twelve dis-tinct relationship domains: (1) indirect external conditions and internal controls, (2) direct external conditions and internal controls, (3) internal mechanisms and controls, (4) indirect external conditions and mediating results, (5) direct external conditions and me-diating results, (6) internal conditions and mediating results, (7) mediating results between environment and internal, (8) mediating results and political management outcomes, (9) mediating results and operational outcomes, (10) political management and opera-tional outcomes, (11) final results, and (12) final results with control variables. The findings reveal that 78% of hypotheses were confirmed, demonstrating significant relationships between strategic maintenance management and organizational performance indicators including cost management, quality strategies, availability optimization, and overall productivity. The study proposes a five-stage strategic maintenance model: diagnostic analysis, strategic conditioning, implementation, evaluation, and strategic feedback. This research contributes to the theoretical development of maintenance engineering by integrating resource depen-dency theory, strategic management theory, and chaos theory into a unified framework for physical asset management. The im-plications extend to Industry 4.0 contexts, where IoT-based predictive maintenance and digital twin technologies are transforming traditional maintenance paradigms.