The Universidad Politécnica Metropolitana de Hidalgo (UPMH, Metropolitan Polytechnic University of Hidalgo) is a public state university in the state of Hidalgo. It was created by an executive order signed on January 9, 2008, and published in the official state newspaper on January 13 of that year.Is a public institution of the State Government, that has a goal of imparting superior education in the levels of degree, technological specialization and other graduate studies, update courses in their different modalities, and also to serve to the development and progress of the society. Doing its function through three substantive areas, which are teaching, research and extension.It began academic activities with 35 students on May 29, 2006, using a leased building in Tolcayuca, located at Carretera Acceso a Tolcayuca, San Javier, with two academic buildings and one for workshops and laboratories, and a capacity of 2,000 students in two shifts. At the beginning of September 2017, the school enrolled 600 students in the four educational programs..
This chapter presents a reproducible computational framework for decision support in smart cities, conceptualized as complex socio-technical systems characterized by interdependent infrastructures and multi-scale dynamics. Traditional urban modeling approaches often rely on static assumptions and fragmented analyses, limiting their ability to capture uncertainty, nonlinearity, and cross-sectoral interactions in real-world environments. This chapter proposes an end-to-end pipeline that integrates surrogate modeling modules conceptually inspired by artificial intelligence paradigms, including forecasting, policy simulation, heuristic evaluation, and probabilistic estimation, within a controlled and reproducible analytical structure. A key contribution is the incorporation of topology-aware validation to assess structural properties such as connectivity, redundancy, and robustness in urban networks. The framework also supports scenario generation and multiobjective evaluation, enabling systematic exploration of trade-offs across efficiency, sustainability, equity, and resilience.
La preparación para entrevistas laborales representa un reto para estudiantes universitarios próximos a egresar, especialmente en áreas tecnológicas donde el dominio técnico debe acompañarse de comunicación clara, seguridad y capacidad para explicar decisiones profesionales. El objetivo de este estudio fue evaluar la influencia de un simulador web basado en el modelo de procesamiento de lenguaje natural DeepSeek-V3.1, en modalidad de razonamiento, para fortalecer la autoconfianza y las competencias comunicativas de estudiantes de noveno cuatrimestre de Ingeniería en Tecnologías de la Información. La investigación tuvo enfoque mixto con predominio cuantitativo, diseño cuasi experimental y alcance longitudinal. Participaron 61 estudiantes de la Universidad Politécnica Metropolitana de Hidalgo, a quienes se aplicaron cuestionarios de pretest y postest, además de una guía de observación de usabilidad. Los resultados mostraron que 75.5% no se percibía preparado para una entrevista laboral; después de utilizar el simulador, 59% reportó mejora en su confianza, 67.2% valoró positivamente la retroalimentación recibida y 75.4% recomendaría la herramienta. Se concluye que la simulación con inteligencia artificial constituye una estrategia extracurricular pertinente para fortalecer la preparación profesional.
Artificial intelligence models deployed in smart cities collect, process, and analyze vast amounts of urban data to optimize services ranging from traffic management to public safety. However, the growing complexity of these models introduces critical concerns regarding their transparency, reliability, and accountability. This chapter addresses three fundamental pillars for responsible AI governance in smart city environments: traceability, which enables systematic tracking of AI model lifecycles and decision provenance; veracity, which ensures the accuracy, integrity, and trustworthiness of data and model outputs; and auditing, which provides structured mechanisms to evaluate AI systems for compliance, fairness, and performance. A mathematical framework grounded in information theory, statistical analysis, and parallel computing theory is developed to formalize these concepts. A high-performance computing approach using Python's multiprocessing framework is presented to demonstrate efficient dataset generation with embedded traceability.
This paper reviews numerical modeling approaches for Dielectric Barrier Discharge (DBD) plasma actuators in aircraft active flow control. While extensive experimental studies exist, a dedicated review of computational methodologies—covering macroscopic, microscopic, and empirical models—has been absent. This work systematically evaluates major models (Shyy, Suzen–Huang, Dorr–Kloker, Roth, Orlov–Corke, Massines), discussing their formulations, assumptions, computational cost, and applicability. It synthesizes simulation studies in aerodynamic applications such as separation control, drag reduction, transition delay, film cooling, and compressor stability. Key findings show that macroscopic models offer a practical balance between accuracy and cost for design-oriented studies, whereas microscopic models provide deeper physical insight at higher expense. The review highlights the effectiveness of DBD actuators in modifying boundary layers, delaying stall, and improving aerodynamic efficiency. Finally, persistent challenges are identified—including energy efficiency, scalability, and model calibration and future directions are suggested, such as hybrid modeling, multi-actuator arrays, and real-time control integration.
This article presents the development of an AI-based adaptive learning prototype enhanced with a ChatGPT-powered generative agent, implemented as a functional web application that personalizes education according to each learner's abilities, pace, and recent performance. Through machine learning, data analytics, and real-time natural language interaction, the system dynamically adapts instructional materials, explains personalized recommendations, and offers formative feedback in human-like language. Built with Python for the backend and Plotly and Dash for the interactive frontend, the prototype embodies the vision of AI-driven independent learning combining data-driven personalization with conversational guidance to foster inclusivity, autonomy, and engagement while redefining the collaboration between educators, learners, and intelligent systems. The article code can be found at: https://github.com/JAIME6609/EDUCATION/blob/main/CODE-EDUCATIONAL-TRANSFORMATION-06.py
