Transpilers refer to a special type of compilation that takes source code and translates it into target source code. This type of technique has been used for different types of implementations in scientific studies. A review of the research areas related to the use of transpilers allows the understanding of the direction in this branch of knowledge. The objective was to carry out an exhaustive and extended mapping of the usage and implementation of transpilers in research studies in the last 10 years. A systematic mapping review was carried out for answering the 5 research questions proposed. The PSALSAR method is used as a guide to the steps needed for the review. In total, from 1181 articles collected, 683 primary studies were selected, reviewed, and analyzed. Proposals from the industry were also analyzed. A new method for automatic data tabulation has been proposed for the mapping objective, using a relational database and SQL language. It was identified that the most common uses of transpilers are related to performance optimizations, parallel programming, embedded systems, compilers, testing, AI, graphics, and software development. In conclusion, it was possible to determine the extent and identification of research sub-areas and their impact on the usage of the transpilers. Future research could be considered about the usage of transpilers in transactional software, migration strategies for legacy systems, AI, math, multiplatform games and apps, automatic source code generation, and networking.

En la actualidad los entornos educativos en línea mediados por tecnologías han desarrollado ambientes de innovación en los procesos pedagógicos que facilitan el aprendizaje de los educandos, la presente investigación tiene como objeto de medir el impacto que tiene el uso de las tecnologías disruptivas en el proceso de enseñanza y aprendizaje de las carreras en línea de UTM online. Con el propósito de integrar estas innovaciones a los centros de Educación Superior, y de proveer nuevos mecanismos de construcción de significados. En cuanto al estudio, parte de una intencionalidad exploratoria, el enfoque utilizado en esta investigación fue de corte cuantitativo con un diseño no experimental y transversal, de alcance descriptivo, realizando un estudio del fenómeno comparado con otras asignaturas. Por otra parte, en los resultados se observa que el 80% de los participantes dieron una calificación correspondiente a totalmente satisfactorio. Lo que demuestra que los participantes perciben un mejor aprendizaje al implementar aplicaciones digitales y plataformas colaborativas en el desarrollo de prácticas en tiempo real discrepando inquietudes. La investigación sugiere que las herramientas digitales como Google Docs, Quizizz y Flipgrid son herramientas de aprendizaje útiles en su adaptación para fomentar la interacción y la retención del aprendizaje en línea.

Unemployment, a significant economic and social challenge, triggers repercussions that affect individual workers and companies, generating a national economic impact. Forecasting the unemployment rate becomes essential for policymakers, allowing them to make short-term estimates, assess economic health, and make informed monetary policy decisions. This paper proposes the innovative GA-LSTM method, which fuses an LSTM neural network with a genetic algorithm to address challenges in unemployment prediction. Effective parameter determination in recurrent neural networks is crucial and a well-known challenge. The research uses the LSTM neural network to overcome complexities and nonlinearities in unemployment predictions, complementing it with a genetic algorithm to optimize the parameters. The central objective is to evaluate recurrent neural network models by comparing them with GA-LSTM to identify the most appropriate model for predicting unemployment in Ecuador using monthly data collected by various organizations. The results demonstrate that the hybrid GA-LSTM model outperforms traditional approaches, such as BiLSTM and GRU, on various performance metrics. This finding suggests that the combination of the predictive power of LSTM with the optimization capacity of the genetic algorithm offers a robust and effective solution to address the complexity of predicting unemployment in Ecuador.

The utilization of software architectures and designs is widespread in software development, offering conceptual frameworks to address recurring challenges. A transpiler is a tool that automatically converts source code from one high-level programming language to another, ensuring algorithmic equivalence. This study introduces an innovative software architecture design model that integrates transpilers into the back-end layer, enabling the automatic transformation of business logic and back-end components from a single source code (the coding artifact) into diverse equivalent versions using distinct programming languages (the automatically produced code). This work encompasses both abstract and detailed design aspects, covering the proposal, automated processes, layered design, development environment, nest implementations, and cross-cutting components. In addition, it defines the main target audiences, discusses pros and cons, examines their relationships with prevalent design paradigms, addresses considerations about compatibility and debugging, and emphasizes the pivotal role of the transpiler. An empirical experiment involving the practical application of this model was conducted by implementing a collaborative to-do list application. This paper comprehensively outlines the relevant methodological approach, strategic planning, precise execution, observed outcomes, and insightful reflections while underscoring the the model’s pragmatic viability and highlighting its relevance across various software development contexts. Our contribution aims to enrich the field of software architecture design by introducing a new way of designing multi-programming-language software.

According to the United Nations (UN), a “continuous, participatory, and inclusive urban planning process should be the starting point and framework for improving population living conditions,” and this research emerges from examining trends in smart cities. It explores the impact of collective intelligence, geographical information systems, and cognitive systems as a way of supporting the decision-making of local governments (municipalities) to face the challenges announced by the UN in the Urban Agenda 2017. This methodology employs an exploratory approach using the participatory action research method. The model was tested in three municipalities in Ecuador using a technological and collaborative platform called Cognitive Urban Planning (PUC). The results highlighted improvements in the decision-making process in urban planning, since the interactive participation processes through the PUC allowed for the suggestion of a group of strategies for an emerging neighborhood plan developed with the citizens working together in the municipality in real-time. In conclusion, the effects of the decision-making plan could be applied to improve and enhance community participation through city co-creation, supporting the decision-making process of municipalities in a way that promotes inclusive urban planning and stakeholders' awareness, as the recommended plans are supported by the desires and needs raised by citizens.