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636,460 artículos
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Año:
2025
ISSN:
2007-1558
Ordaz Oliver, Mario Oscar; Espejel Rivera, María Angélica; Hernandez Perez, Javier; Gutiérrez Moreno, Evelin; Ordaz Oliver, Jesús Patricio; Hernández Hernández, Amadeo Manuel
Editorial Académica Dragón Azteca
Resumen
This project details the implementation of an artificial neural network (ANN) as the principal element of a system engineered to identify and predict future control states of a thrust-propelled seesaw in an open-loop configuration. The primary objective was to maintain the seesaw in a balanced 90° position. The system’s dynamic behaviour was analysed under minimal external disturbances, facilitating development and evaluation in a controlled environment. Experimental data were captured via a programmable Arduino UNO board transmitting over the serial port and recorded in an Excel file for subsequent processing. A Kalman filter was applied to refine the data, from which a random subset was selected to train the neural network. A comprehensive analysis of the results is presented herein, demonstrating the ANN’s satisfactory performance in the control task.
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Año:
2025
ISSN:
2007-1558
Ordaz Oliver, Mario Oscar; Ortiz Licona, Alberto; Espejel Rivera, María Angélica; Hernández Hernández , Amadeo Manuel
Editorial Académica Dragón Azteca
Resumen
In this study, a numerical analysis of the mathematical model of a single-phase transformer is presented, incorporating a comprehensive expression for the mutual magnetic flux to assess its effect on energy transfer efficiency. Drawing on Faraday’s and Ampère’s laws, the fundamental relationships between magnetic flux, currents, and voltages in the windings are established. Through numerical simulations, the influence of mutual leakage flux is investigated, alongside structural parameters such as resistance, reactance, and the number of turns. The results obtained confirm the model’s effectiveness in predicting transformer behaviour under varying load and network conditions, offering an analytical tool for the design and enhancement of these electrical machines. This approach not only deepens the theoretical understanding of transformers but also supports the development of more efficient and reliable electrical systems.
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Año:
2025
ISSN:
2007-1558
Salas López, Julio Cesar; Zarazúa Silva, Juvencio Sebastián; Ruiz-Vanoye, Jorge A.; Simancas-Acevedo, Eric; Salgado-Ramírez, Julio C.; Díaz-Parra, Ocotlán
Editorial Académica Dragón Azteca
Resumen
Air quality in Guadalajara has deteriorated in recent years, becoming a serious health concern for the local population. In response, this project seeks to mitigate the impact of pollution by developing a prediction platform based on ARIMA models implemented in Python.
The system will analyse historical pollutant levels—including PM₂.₅, PM₁₀, SO₂, NO₂, O₃ and CO—enabling the anticipation of high-pollution episodes. Armed with this information, both citizens and authorities will be able to take timely preventative measures.
Given the growing interest in air quality and its implications for health, this tool will furnish valuable data for informed decision-making. Moreover, it will facilitate trend analysis and permit short-term forecasts, helping to identify potential pollution episodes before they occur.
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Año:
2025
ISSN:
2007-1558
Cervantes Reyes, Jesus Eduardo; Alamilla Daniel, Ma. De Los Ángeles; Lícona Rodriguez, Ángel Ricardo; Ramirez Dominguez, Elihu Hadad
Editorial Académica Dragón Azteca
Resumen
Robot-assisted rehabilitation effectively enhances motor recovery in patients with mobility impairments. This study examines the REHAP system—a two-DOF mechatronic rehabilitation device for passive physiotherapy—focusing on dynamic modelling and energy-efficiency analysis. The Euler–Lagrange method was employed to derive the dynamic model, incorporating actuator parameters obtained through experimental characterisation of DC motors. We assessed energy consumption under various control strategies and mechanical-loading conditions. Results indicate that the choice of control strategy and the tuning of actuator parameters significantly impact system efficiency, highlighting the critical need for accurate model calibration. Integrating dynamic modelling improves both motion precision and energy economy, thereby enabling more sustainable rehabilitation technologies. This research underscores how energy-aware control strategies can enhance both performance and sustainability in robotic physiotherapy systems.
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Año:
2025
ISSN:
2007-1558
Zarate-Zapata, Aldo-César; Gibaja-Romero, Damián-Emilio; Sánchez-Partida, Diana; Martínez-Flores, José-Luis
Editorial Académica Dragón Azteca
Resumen
The location of vaccination centers exceeds the challenges imposed by the COVID-19 pandemic on academicians and practitioners. Given the importance of reducing population mobility during such a phenomenon, such a challenge arose. Specifically, centers in low-demand areas motivate people’s mobility to get vaccinated, increasing contagion. In this document, we analyze the allocation of vaccination centers in Puebla, Mexico, to propose their relocation by closing some existing facilities in low-demand areas and opening new ones in regions with higher demand. For such an end, we analyze different scenarios by considering uncertain changes in regions’ vaccine demand. We apply the Gravity location model to relocate and minimize the distance between the region’s population and its vaccination center.
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Año:
2025
ISSN:
2007-1558
Ochoa-Montiel, Rocío; Sánchez-López, Carlos; Montalvo-Galicia, Fredy
Editorial Académica Dragón Azteca
Resumen
Timely detection of diseases in various crops is a necessary task to ensure sufficient production of food sources. Visual analysis by an expert is the method traditionally used for this activity, so it is subjective and prone to errors.
In this paper, we propose a color segmentation method and a feature analysis for the recognition of rice crop leaves using machine learning. We use balanced sets of images and propose a set of experiments that allow us to discover the features that influence the classification indices, like the need to identify more precise characteristics for the classes of similar leaves or the disease regions. Results show that some features of texture and color are irrelevant for disease recognition.
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Año:
2025
ISSN:
2007-1558
Ruiz Jaimes, Miguel Ángel; Ruiz-Vanoye, Jorge A.; Flores Sedano, Juan José; Toledo-Navarro, Yadira
Editorial Académica Dragón Azteca
Resumen
At the Autonomous University the current wireless network infrastructure is insufficient to meet the growing demand for access, causing failures and intermittencies in the service. Faced with this problem, a thesis proposal has been developed to implement a modern wireless network with high user density, centralized management and improved security schemes. The proposed methodology for the implementation of a high-density wireless network in a higher education institution. In conclusion, the project made it possible to comply with the hypothesis proposed since the implementation of a robust wireless network, with adequate levels of security and profile management, facilitated the ubiquitous access of online academic resources by the student and teaching community of the university. This translates into support for educational quality.
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Año:
2025
ISSN:
2007-1558
Ruiz Jaimes, Miguel Ángel; López Luna, Julio C.; Flores Sedano, Juan José; Toledo-Navarro, Yadira
Editorial Académica Dragón Azteca
Resumen
Dengue is endemic in Mexico, with epidemic cycles occurring approximately every three to five years, associated with the introduction of new viral serotypes into susceptible populations. This study analysed the relationship between climatological variables and dengue incidence in Morelos, Mexico, during the period 1999–2009, aiming to identify favourable conditions to inform territorially focused prevention and control measures, using spatial analysis methods. The analysis of the influence of climate on dengue transmission in Morelos demonstrated the importance of precipitation in developing early warning systems for potential epidemics. It is recommended that territorially focused preventive strategies be implemented, such as the elimination of breeding sites, fumigation, and social mobilisation in areas at risk due to environmental, social, and epidemiological factors.
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Año:
2025
ISSN:
2007-1558
Scanlon, Brian; Quille, Keith; Jaiswal, Rajesh
Editorial Académica Dragón Azteca
Resumen
This study aims to assess the validity and precision of employing a multivariate LSTM model compared to traditional models and stock analysis techniques for predicting the price of the cryptocurrency BTC. The research incorporates a feature elimination technique to optimize price predictions across various time intervals by removing non-essential and redundant features, including economic factors. In the case of BTC, with a finite total supply of 21 million coins, an increase in popularity generally leads to a surge in price. To gauge BTC’s popularity, tweet frequency and Google search trends were considered as input factors. Additionally, traditional indicators like USD, Gold and the Volatility Index (VIX) were used to measure the stock market atmosphere. The LSTM model’s performance was benchmarked against other models such as RNNs, ANN, SVR and ARIMA. The LSTM model exhibiting superior learning in multivariate data, achieving an RMSE score of 268.83.
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Año:
2025
ISSN:
2007-1558
Ortiz-Suarez, Luis Arturo; Ruiz Vanoye, Jorge A.; Trejo-Macotela, Francisco Rafael
Editorial Académica Dragón Azteca
Resumen
In this article, an approach for optimizing energy consumption in smart buildings using machine learning algorithms is presented. Utilizing TDSP, data on climatic conditions, occupancy, and energy consumption obtained from EnergyPlus software are integrated. Feature selection and feature importance techniques, as well as statistical analyses, are implemented to select variables that are used to train machine learning models such as MLP neural networks, support vector machines, random forest, and XGBRegressor for predicting energy consumption, with accuracy evaluated using RMSE. It was demonstrated that models based on neural networks offer better accuracy, thereby enabling measures to achieve energy optimization.
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Hecho en México, Universidad Nacional Autónoma de México (UNAM). 1997-2025.
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