El propósito de la investigación es conocer los resultados en el aprendizaje de los estudiantes de nuevo ingreso a la Unidad Académica de Ingeniería Eléctrica de la Universidad Autónoma de Zacatecas, como resultado de recibir por más de dos años clases a distancia durante la Pandemia por COVID 19. El muestreo es probabilístico, aleatorio simple, integrado por 150 estudiantes. La crisis sanitaria internacional ocasionada por el virus SARS COV2, afectó las actividades cotidianas, entre ellas la educación. En México fue necesario declarar emergencia sanitaria, se establecieron medidas de sana distancia que incluían la suspensión de clases presenciales en todos los niveles educativos, por lo que la Secretaría de Educación estableció las actividades académicas a distancia a través del uso de las Tecnologías de la Información TICs, debido a lo anterior Pérez Archundía E. 2021, considera que el rezago tecnológico y de alfabetización tecnológica que prevalece en el país, así como la desigualdad social y económica de los estudiantes sería un problema. El estudio pretende conocer las principales dificultades que enfrentaron los estudiantes durante la educación a distancia, en qué materias experimentan más rezago y con base a los resultados proponer una estrategia para su regularización académica.

This study presents an efficient and reproducible framework for estimating wind power density (WPD) across Mexico using a Dense Neural Network (DNN) trained exclusively on ERA5 and ERA5-Land reanalysis data. The model is designed as a computationally efficient surrogate that reproduces the statistical behavior of the ERA5 benchmark while enabling national-scale WPD mapping and short-term projections at minimal computational cost. Meteorological variables—including wind components at 10 m and 100 m, surface temperature, pressure, and terrain elevation—were harmonized on a 0.25° grid for the 1971–2024 period. A chronological dataset split (70-20-10%) was applied to realistically evaluate forecasting capability. The optimized DNN architecture (512-256-128 neurons) achieved high predictive performance (R² ≈ 0.91, RMSE ≈ 6.2 W/m²) and accurately reproduced spatial patterns and seasonal variability, particularly in high-resource regions such as Oaxaca and Baja California. Compared with deeper neural architectures, the proposed model reduced training time by more than 60% and energy consumption by approximately 40%, supporting principles of sustainable computing and Industry 5.0. The resulting WPD fields, delivered in interoperable NetCDF formats, can be directly integrated into decision-support tools for wind-farm planning, smart-grid management, and long-term renewable-energy strategies in data-scarce environments.

Calving in cattle is divided into two main stages: dilation and expulsion, during which timely assistance can reduce reproductive losses. This study presents a smart ear-mounted device as a proof-of-concept heart-rate monitoring platform for calving-stage assessment in dairy cows. The prototype preserves the form factor of a conventional ear tag and integrates a MAX30105 optical sensor, an Arduino Nano microcontroller, local micro-SD storage, and an autonomous power supply. Field tests were conducted in Holstein cows at Rancho El Pinar, Trancoso, Zacatecas, Mexico. Heart rate was recorded every 10 min and grouped according to physiological stages around calving. The results showed distinctive heart rate patterns, with higher values during dilation and lower values after delivery, supporting the use of ear-mounted heart rate monitoring as a non-invasive descriptive marker of stage-related physiological variation around labor. An average temperature profile from 70 h before to 50 h after calving was also incorporated as complementary descriptive evidence of peripartum physiological variation. Because heart rate is a non-specific physiological variable affected by stress, movement, ambient temperature, feeding, health status, and sensor contact, the present study does not propose HR as a stand-alone or definitive predictor of calving or dystocia. Instead, the device is presented as a proof-of-concept platform for future multi-indicator monitoring and validation studies. The proposed system is presented as a proof-of-concept invention that combines a practical wearable format with physiological monitoring and a conceptual decision-support logic that remains to be validated and integrated with additional indicators before any field implementation.

La utilización de los sistemas multimedia en la educación, responde a una nueva concepción en la enseñanza como un proceso no lineal, y a la integración de texto, imágenes y sonido, habitualmente bajo el control de un ordenador. Debido a las aportaciones de la multimedia y la forma en que se aprovecha e integran diversos medios, para transmitir información; se diseñó y creó un sistema interactivo multimedia utilizando las siguientes herramientas para producción multimedia como: Neobook 5.7, Fireworks, Flash y Astro Flash Creator. El sistema está formado por una pantalla de inicio, la cual nos enlaza al menú principal del sistema multimedia, la pantalla de menú principal nos permite navegar en el sistema y tiene las opciones siguientes; Glosario, Objetivo, Contenido, Actividades de repaso, Rompecabezas, Para más información, Requisitos del Sistema, Ayuda, Referencias y Galería. De esta manera los usuarios podrán realizar actividades que les permitan interactuar con el sistema multimedia y lograrán asimilar conocimientos de una mejor manera sobre el tema de los Tornados. DOI:https://doi.org/10.56238/futuroeducpesqutrans-033

This manuscript presents a patent-based engineering case study of a compact LiBr–H₂O evaporator–absorber unit, tracing its progression from conceptual embodiment to laboratory validation through contextualized technology-readiness assessment and bounded data-driven analysis. The study reconstructs three successive prototypes, applies the international Technology Readiness Level (TRL) framework together with the Mexican TRL guideline, and includes an exploratory artificial neural network (ANN) analysis of the validated Prototype III. The results show a maturation pathway: Prototype I corresponded to conceptual embodiment, Prototype II provided proof-of-concept evidence through observable cooling, and Prototype III was a protected, instrumented, and experimentally validated laboratory prototype, conservatively assigned to TRL 4 as the highest level supported by the evidence. The auxiliary ANN gave only a limited approximation of the thermal response (mean test R² = 0.41, dispersion ±0.32), consistent with a single-run laboratory-scale dataset rather than robust predictive modeling. Overall, the study integrates prototype trajectory, intellectual-property protection, TRL assessment, and bounded analysis into a rigorous evaluation of a protected pre-commercial thermal subsystem. Beyond this device, the framework offers inventors, designers, and research groups a transferable reference, letting them build on a documented pathway rather than restarting from the lowest readiness levels and identifying the evidence needed for each TRL transition.

Organic dyes, such as Brilliant Cresyl Blue (BCB), are among the most persistent and difficult-to-treat water pollutants, primarily originating from textile factories that discharge enormous quantities of polluted water daily. Treating these large volumes requires readily available and durable materials. Palm waste, and more specifically, date palm leaves (DPL), is one of the least utilized materials due to a lack of awareness about it. To gain deeper insights into the material's structural and chemical properties, the biosorbent was thoroughly characterized before and after adsorption using X-ray diffraction (XRD), scanning electron microscopy (SEM), and Fourier transform infrared spectroscopy (FTIR). The value of the elemental charge of the absorbent material was also determined, and it was found to be 7.34, which expresses the almost neutral nature of the material's surface. The adsorption process was systematically studied by evaluating the influence of key operating parameters, including adsorbent mass, solution pH, and contact time. These parameters were systematically investigated and optimized using the Box-Behnken Design (BBD) coupled with Response Surface Methodology (RSM). Numerical optimization via the desirability function identified the ideal conditions as an adsorbent mass of 0.079 g, a solution pH of 9.17, and a contact time of 42.01 minutes, yielding an impressive removal efficiency of 98.15%. Furthermore, the comparative evaluation of the modeling approaches revealed the superior predictive performance of the Artificial Neural Network (ANN) over the Response Surface Methodology (RSM). This was clearly demonstrated by the significantly lower error metrics obtained with the ANN model across all evaluated parameters. Specifically, the ANN yielded a mean squared error (MSE) of 0.4022 vs 4.8427 for RSM, a mean absolute error (MAE) of 0.1538 versus 1.5994, a mean absolute percentage error (MAPE) of 0.17% versus 2.32%, and a standard deviation of error (SDE) of 0.6342 vs 2.2683. These findings confirm the enhanced accuracy, consistency, and generalization capability of the ANN model for simulating the adsorption process, making it a more robust tool for predictive modeling and process optimization in dye-removal applications. The results underscore the effectiveness and sustainability of date palm leaves (DPL) as a promising biosorbent for eliminating Brilliant Cresyl Blue (BCB) from polluted water, thereby supporting its practical applicability in large-scale environmental remediation efforts.