Pre-conceptual schemas are a straightforward way to represent knowledge using controlled language regardless of context. Despite the benefits of using pre-conceptual schemas by humans, they present challenges when interpreted by computers. We propose an approach to making computers able to interpret the basic pre-conceptual schemas made by humans. To do that, the construction of a linguistic corpus is required to work with large language models—LLM. The linguistic corpus was mainly fed using Master’s and doctoral theses from the digital repository of the University of Nariño to produce a training dataset for re-training the BERT model; in addition, we complement this by explaining the elicited sentences in triads from the pre-conceptual schemas using one of the cutting-edge large language models in natural language processing: Llama 2-Chat by Meta AI. The diverse topics covered in these theses allowed us to expand the spectrum of linguistic use in the BERT model and empower the generative capabilities using the fine-tuned Llama 2-Chat model and the proposed solution. As a result, the first version of a computational solution was built to consume the language models based on BERT and Llama 2-Chat and thus automatically interpret pre-conceptual schemas by computers via natural language processing, adding, at the same time, generative capabilities. The validation of the computational solution was performed in two phases: the first one for detecting sentences and interacting with pre-conceptual schemas with students in the Formal Languages and Automata Theory course—the seventh semester of the systems engineering undergraduate program at the University of Nariño’s Tumaco campus. The second phase was for exploring the generative capabilities based on pre-conceptual schemas; this second phase was performed with students in the Object-oriented Design course—the second semester of the systems engineering undergraduate program at the University of Nariño’s Tumaco campus. This validation yielded favorable results in implementing natural language processing using the BERT and Llama 2-Chat models. In this way, some bases were laid for future developments related to this research topic.

This study presents a systematic literature review on the application of artificial intelligence (AI) in vehicle license plate recognition, focusing on neural network-based technologies. The primary objective is to identify recent advancements that enhance traffic control automation and road safety. The research methodology involves a structured search and analysis of 90 significant publications selected from databases such as IEEE Xplore, ScienceDirect, Scopus, and DOAJ. Findings indicate that convolutional neural networks (CNNs) and deep learning models play a crucial role in improving recognition accuracy and efficiency, particularly through optimized image processing techniques and convolutional layers. However, challenges persist due to variations in license plate design and adverse environmental conditions affecting system performance. The study highlights the need for continued research on image preprocessing methods to enhance robustness and adaptability. The conclusions emphasize the critical role of AI-driven recognition systems in modern transportation infrastructure, advocating for further integration of advanced neural network architectures. From a practical perspective, these findings contribute to the development of more reliable and efficient vehicle identification systems, with implications for law enforcement, automated tolling, and smart city initiatives.

Blockchain, una estructura de datos que registra transacciones de manera segura y eficiente destaca el papel fundamental en la creación de plataformas descentralizadas. Esta tecnología emergente está transformando el panorama de la transacción de energía, mejorando la eficiencia, la fiabilidad y la sostenibilidad de los sistemas energéticos. En este estudio se llevó a cabo una Revisión Sistemática de la Literatura (RSL) centrada en las tecnologías empleadas en las transacciones de energía eléctrica. Se consultaron bases de datos especializadas, como Scopus, IEEE, ACM y Google Scholar. Se resalta el papel crucial de la tecnología Blockchain, la cual está siendo ampliamente adoptada gracias a su enfoque en la descentralización y su habilidad para establecer registros inalterables y transparentes. Se detectaron diversos elementos fundamentales en estas operaciones, tales como nodos, contratos inteligentes y algoritmos de optimización, que posibilitan el funcionamiento eficaz y descentralizado de la red, asegurando la seguridad y confiabilidad de las transacciones.

This study focuses on the formal verification of a parallel version of the minimax algorithm using the mCRL2 modeling language, applied to the game of Connect 4. The research aims to ensure that the algorithm behaves correctly in concurrent execution environments by providing a formal model and conducting rigorous verification. The parallel version of minimax distributes computations across multiple threads, with each thread evaluating different successor states concurrently. Using mCRL2, we specify the algorithm’s behavior, generate Labeled Transition Systems (LTSs), and verify critical properties such as the absence of deadlocks, liveness, and correctness of state transitions. The formal verification process demonstrates that the proposed model accurately represents the parallel minimax algorithm and ensures its reliability by verifying properties that guarantee unique and non-redundant actions throughout the execution. The findings highlight the value of formal methods in validating the correctness of parallel artificial intelligence algorithms, laying the foundation for future optimizations that focus on performance.

The exchange of medical information significantly benefits people’s quality of life, improving their care and treatment. The interoperability of the entire healthcare ecosystem is a constant challenge. Blockchain technology is an alternative to find a balance in the healthcare ecosystem. Smart contracts (SC) are decentralized and self-executing programs that allow the automation of agreements without intermediaries to improve operational efficiency. However, the constant development of new Blockchain technologies and programming languages for smart contracts is a growing problem. This work presents the validation by expert judgment of the MUISCA (Mechanism for UnIversal SmartContrAct) tool, which uses Model Driven Engineering (MDE). MUISCA uses transformations of models and models to text to generate smart contracts in healthcare environments and specific to Blockchain platforms. The validation is conducted by smart contracts development experts, who show positivity in the perceived usefulness.

This paper introduces a new approach for the analysis of didactic transposition phenomenon in Computer Science education, and its foundations for the construction of teaching objects. Thus, two main scenarios were explored: teaching experiences on programming fundamentals at undergraduate level, and the construction and deployment of teaching objects which have been framed within the European project called Open Discovery Space for K-12 education, by using computing means. The description of experiences in both scenarios is documented according to the following schema: A theoretical background, the findings, and the statement of new proposals to improve teaching practices and the criteria for constructing teaching objects as conclusions of this paper.