Modelo Pedagógico-terapéutica para atención a la educación especial en Guatemala
Artículo Materias > Psicología Universidad Internacional Iberoamericana México > Investigación > Artículos y libros Abierto Inglés, Español La población con necesidades educativas especiales ha enfrentado a lo largo de la historia dificultades en la inclusión social, cultural y también educativa. Guatemala no es la excepción por ser un país en vías de desarrollo y con grandes deficiencias de atención en el sistema educativo. Actualmente se carece de una pedagogía y didáctica que solucione esta problemática para los docentes que trabajan con alumnos con necesidades educativas especiales, como a la vez los centros educativos carecen de un adiestramiento en relación con educación especial junto a programas terapéuticos que brinden resultados para las personas con discapacidad. En esta investigación se analizó la validez del modelo pedagógico-terapéutico “Cetumismo” contra el programa “Aula Recurso” del Ministerio de Educación de Guatemala. Esto se efectuó en una muestra seleccionada de docentes que laboran en centros educativos, luego de responder un Cuestionario estandarizado para valorar la calidad de la Educación Especial en los centros educativos por medio del proceso estadístico prueba t de student, en donde se compararon las medias obtenidas en los dos momentos de evaluación, obteniendo la varianza. Los resultados obtenidos establecen que con un nivel de significancia de 0.05 se rechaza la hipótesis nula y se acepta la hipótesis alternativa; por lo que la comparación de las medias en el proceso estadístico realizado determina que, entre ambos programas, el programa que resuelve las necesidades educativas especiales en relación con la educación especial es Modelo Pedagógico Terapéutico “Cetumismo”. Los docentes evaluados a pesar de pertenecer a una escuela que utiliza el programa “Aula Recurso” impuesto por el Ministerio de Educación de Guatemala, consideran que un modelo pedagógico-terapéutico como “Cetumismo” traería mayores beneficios en las necesidades de educación especial de personas con discapacidad, con adecuaciones curriculares específicas para cada alumno, capacitaciones constantes y actualizadas para docentes, pensum diferenciado, proceso educativo inclusivo luego del alcance de las competencias propuestas por caso metadata Soto Genovese, Eimy Ann mail SIN ESPECIFICAR (2020) Modelo Pedagógico-terapéutica para atención a la educación especial en Guatemala. MLS Psychology Research, 3 (1). pp. 39-64. ISSN 26055295
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La población con necesidades educativas especiales ha enfrentado a lo largo de la historia dificultades en la inclusión social, cultural y también educativa. Guatemala no es la excepción por ser un país en vías de desarrollo y con grandes deficiencias de atención en el sistema educativo. Actualmente se carece de una pedagogía y didáctica que solucione esta problemática para los docentes que trabajan con alumnos con necesidades educativas especiales, como a la vez los centros educativos carecen de un adiestramiento en relación con educación especial junto a programas terapéuticos que brinden resultados para las personas con discapacidad. En esta investigación se analizó la validez del modelo pedagógico-terapéutico “Cetumismo” contra el programa “Aula Recurso” del Ministerio de Educación de Guatemala. Esto se efectuó en una muestra seleccionada de docentes que laboran en centros educativos, luego de responder un Cuestionario estandarizado para valorar la calidad de la Educación Especial en los centros educativos por medio del proceso estadístico prueba t de student, en donde se compararon las medias obtenidas en los dos momentos de evaluación, obteniendo la varianza. Los resultados obtenidos establecen que con un nivel de significancia de 0.05 se rechaza la hipótesis nula y se acepta la hipótesis alternativa; por lo que la comparación de las medias en el proceso estadístico realizado determina que, entre ambos programas, el programa que resuelve las necesidades educativas especiales en relación con la educación especial es Modelo Pedagógico Terapéutico “Cetumismo”. Los docentes evaluados a pesar de pertenecer a una escuela que utiliza el programa “Aula Recurso” impuesto por el Ministerio de Educación de Guatemala, consideran que un modelo pedagógico-terapéutico como “Cetumismo” traería mayores beneficios en las necesidades de educación especial de personas con discapacidad, con adecuaciones curriculares específicas para cada alumno, capacitaciones constantes y actualizadas para docentes, pensum diferenciado, proceso educativo inclusivo luego del alcance de las competencias propuestas por caso
| Tipo de Documento: | Artículo |
|---|---|
| Palabras Clave: | inclusión, psicología educativa, educación especial, aula recurso |
| Clasificación temática: | Materias > Psicología |
| Divisiones: | Universidad Internacional Iberoamericana México > Investigación > Artículos y libros |
| Depositado: | 08 Jul 2022 23:30 |
| Ultima Modificación: | 08 Jul 2022 23:30 |
| URI: | https://repositorio.unini.edu.mx/id/eprint/2632 |
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Edge-Based Autonomous Fire and Smoke Detection Using MobileNetV2
Forest fires pose significant threats to ecosystems, human life, and the global climate, necessitating rapid and reliable detection systems. Traditional fire detection approaches, including sensor networks, satellite monitoring, and centralized image analysis, often suffer from delayed response, high false positives, and limited deployment in remote areas. Recent deep learning-based methods offer high classification accuracy but are typically computationally intensive and unsuitable for low-power, real-time edge devices. This study presents an autonomous, edge-based forest fire and smoke detection system using a lightweight MobileNetV2 convolutional neural network. The model is trained on a balanced dataset of fire, smoke, and non-fire images and optimized for deployment on resource-constrained edge devices. The system performs near real-time inference, achieving a test accuracy of 97.98% with an average end-to-end prediction latency of 0.77 s per frame (approximately 1.3 FPS) on the Raspberry Pi 5 edge device. Predictions include the class label, confidence score, and timestamp, all generated locally without reliance on cloud connectivity, thereby enhancing security and robustness against potential cyber threats. Experimental results demonstrate that the proposed solution maintains high predictive performance comparable to state-of-the-art methods while providing efficient, offline operation suitable for real-world environmental monitoring and early wildfire mitigation. This approach enables cost-effective, scalable deployment in remote forest regions, combining accuracy, speed, and autonomous edge processing for timely fire and smoke detection.
Dilshod Sharobiddinov mail , Hafeez Ur Rehman Siddiqui mail , Adil Ali Saleem mail , Gerardo Méndez Mezquita mail , Debora L. Ramírez-Vargas mail debora.ramirez@unini.edu.mx, Isabel de la Torre Díez mail ,
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Breast cancer is a lethal carcinoma impacting a considerable number of women across the globe. While preventive measures are limited, early detection remains the most effective strategy. Accurate classification of breast tumors into benign and malignant categories is important which may help physicians in diagnosing the disease faster. This survey investigates the emerging inclination and approaches in the area of machine learning (ML) for the diagnosis of breast cancer, pointing out the classification techniques based on both segmentation and feature selection. Certain datasets such as the Wisconsin Diagnostic Breast Cancer Dataset (WDBC), Wisconsin Breast Cancer Dataset Original (WBCD), Wisconsin Prognostic Breast Cancer Dataset (WPBC), BreakHis, and others are being evaluated in this study for the demonstration of their influence on the performance of the diagnostic tools and the accuracy of the models such as Support vector machine, Convolutional Neural Networks (CNNs) and ensemble approaches. The main shortcomings or research gaps such as prejudice of datasets, scarcity of generalizability, and interpretation challenges are highlighted. This research emphasizes the importance of the hybrid methodologies, cross-dataset validation, and the engineering of explainable AI to narrow these gaps and enhance the overall clinical acceptance of ML-based detection tools.
Alveena Saleem mail , Muhammad Umair mail , Muhammad Tahir Naseem mail , Muhammad Zubair mail , Silvia Aparicio Obregón mail silvia.aparicio@uneatlantico.es, Rubén Calderón Iglesias mail ruben.calderon@uneatlantico.es, Shoaib Hassan mail , Imran Ashraf mail ,
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Ultra Wideband radar-based gait analysis for gender classification using artificial intelligence
Gender classification plays a vital role in various applications, particularly in security and healthcare. While several biometric methods such as facial recognition, voice analysis, activity monitoring, and gait recognition are commonly used, their accuracy and reliability often suffer due to challenges like body part occlusion, high computational costs, and recognition errors. This study investigates gender classification using gait data captured by Ultra-Wideband radar, offering a non-intrusive and occlusion-resilient alternative to traditional biometric methods. A dataset comprising 163 participants was collected, and the radar signals underwent preprocessing, including clutter suppression and peak detection, to isolate meaningful gait cycles. Spectral features extracted from these cycles were transformed using a novel integration of Feedforward Artificial Neural Networks and Random Forests , enhancing discriminative power. Among the models evaluated, the Random Forest classifier demonstrated superior performance, achieving 94.68% accuracy and a cross-validation score of 0.93. The study highlights the effectiveness of Ultra-wideband radar and the proposed transformation framework in advancing robust gender classification.
Adil Ali Saleem mail , Hafeez Ur Rehman Siddiqui mail , Muhammad Amjad Raza mail , Sandra Dudley mail , Julio César Martínez Espinosa mail ulio.martinez@unini.edu.mx, Luis Alonso Dzul López mail luis.dzul@uneatlantico.es, Isabel de la Torre Díez mail ,
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Background: Physical activity in children and adolescents represents one of the most important lifestyle factors to determine current and future health. Aim: The aim of the study is to assess the lifestyle and dietary factors linked to physical activity in younger populations across five countries in the Mediterranean region. Design: A total of 2,011 parents of children and adolescents (age range 6–17 years) participating to a preliminary survey of the DELICIOUS project were investigated to determine children's adequate physical activity level (identified using the short form of the international physical activity questionnaire) as well as diet quality parameters [measured as Youth-Healthy Eating Index (Y-HEI)] and eating and lifestyle factors (i.e., meal habits, sleep duration, screen time, etc.). Logistic regression analyses were performed to assess the odds ratios (ORs) and 95% confidence intervals (CIs) for the associations between variables of interest. Results: Younger children of younger parents currently working had higher rates and probability to have adequate physical activity. Multivariate analysis showed that children and adolescents who had breakfast (OR = 1.88, 95% CI: 1.38, 2.56) and often ate with their family (OR = 1.80, 95% CI: 0.90, 3.61) were more likely to have an adequate level of physical activity. Children and adolescents who reported a sleep duration (8–10 h) closest to the recommended one were significantly more likely to achieve adequate levels of physical activity (OR = 1.88, 95% CI: 1.38, 2.56). Conversely, those with more than 4 h of daily screen time were less likely to engage in adequate physical activity (OR = 0.77, 95% CI: 0.54, 1.10). Furthermore, children and adolescents in the highest tertile of YEHI scores showed a 60% greater likelihood of engaging in adequate physical activity (OR = 1.60, 95% CI: 1.27, 2.01). Conclusion: These results emphasize the importance of promoting healthy diet and lifestyle habits, including structured and high quality shared meals, sufficient sleep, and screen time moderation, as key strategies to support active behaviors in younger populations. Future interventions should focus on reinforcing these behaviors through parental guidance and community-based initiatives to foster lifelong healthy habits.
Alice Rosi mail , Francesca Scazzina mail , Maria Antonieta Touriz Bonifaz mail , Francesca Giampieri mail francesca.giampieri@uneatlantico.es, Achraf Ammar mail , Khaled Trabelsi mail , Osama Abdelkarim mail , Mohamed Aly mail , Evelyn Frias-Toral mail , Juancho Pons mail , Laura Vázquez-Araújo mail , Josep Alemany Iturriaga mail josep.alemany@uneatlantico.es, Lorenzo Monasta mail , Nunzia Decembrino mail , Ana Mata mail , Adrián Chacón mail , Pablo Busó mail , Giuseppe Grosso mail ,
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Polyphenols are naturally occurring compounds that can be found in plant-based foods, including fruits, vegetables, nuts, seeds, herbs, spices, and beverages, the use of which has been linked to enhanced brain health and cognitive function. These natural molecules are broadly classified into two main groups: flavonoids and non-flavonoid polyphenols, the latter including phenolic acids, stilbenes, and tannins. Flavonoids are primarily known for their potent antioxidant properties, which help neutralize harmful reactive oxygen species (ROS) in the brain, thereby reducing oxidative stress, a key contributor to neurodegenerative diseases. In addition to their antioxidant effects, flavonoids have been shown to modulate inflammation, enhance neuronal survival, and support neurogenesis, all of which are critical for maintaining cognitive function. Phenolic acids possess strong antioxidant properties and are believed to protect brain cells from oxidative damage. Neuroprotective effects of these molecules can also depend on their ability to modulate signaling pathways associated with inflammation and neuronal apoptosis. Among polyphenols, hydroxycinnamic acids such as caffeic acid have been shown to enhance blood-brain barrier permeability, which may increase the delivery of other protective compounds to the brain. Another compound of interest is represented by resveratrol, a stilbene extensively studied for its potential neuroprotective properties related to its ability to activate the sirtuin pathway, a molecular signaling pathway involved in cellular stress response and aging. Lignans, on the other hand, have shown promise in reducing neuroinflammation and oxidative stress, which could help slow the progression of neurodegenerative diseases and cognitive decline. Polyphenols belonging to different subclasses, such as flavonoids, phenolic acids, stilbenes, and lignans, exert neuroprotective effects by regulating microglial activation, suppressing pro-inflammatory cytokines, and mitigating oxidative stress. These compounds act through multiple signaling pathways, including NF-κB, MAPK, and Nrf2, and they may also influence genetic regulation of inflammation and immune responses at brain level. Despite their potential for brain health and cognitive function, polyphenols are often characterized by low bioavailability, something that deserves attention when considering their therapeutic potential. Future translational studies are needed to better understand the right dosage, the overall diet, the correct target population, as well as ideal formulations allowing to overcome bioavailability limitations.
Justyna Godos mail , Giuseppe Carota mail , Giuseppe Caruso mail , Agnieszka Micek mail , Evelyn Frias-Toral mail , Francesca Giampieri mail francesca.giampieri@uneatlantico.es, Julién Brito Ballester mail julien.brito@uneatlantico.es, Maurizio Battino mail maurizio.battino@uneatlantico.es, Carmen Lilí Rodríguez Velasco mail carmen.rodriguez@uneatlantico.es, José L. Quiles mail jose.quiles@uneatlantico.es,
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