Planteamiento de un modelo energético descriptivo aplicable a la instalación de sistemas solares fotovoltaicos interconectados a la red mediante generación distribuida: caso de estudio en Nuevo Laredo
Artículo Materias > Ingeniería Universidad Internacional Iberoamericana México > Investigación > Artículos y libros Abierto Inglés, Español El consumo de energía a nivel mundial continúa incrementándose debido al aumento demográfico y desarrollo tecnológico, sin embargo, el 73 % de la energía utilizada proviene de fuentes fósiles altamente contaminantes para el planeta y cuyas reservas mundiales se reducen aceleradamente, utilizando únicamente un porcentaje menor de energías limpias o renovables que mitiguen el calentamiento global, el cambio climático y aseguren la sustentabilidad energética mundial. Conscientes de esta problemática nacional y mundial, se propone un modelo energético descriptivo que incluya metodológicamente los pasos a seguir para determinar la viabilidad de instalar sistemas solares fotovoltaicos en cualquier región del mundo, mediante el análisis del recurso energético renovable disponible, de las variables medioambientales y eléctricas y, de los recursos humanos, materiales y financieros. El modelo propuesto se desarrolla y diseña mediante la recopilación, integración y análisis de diversas fuentes y trabajos de investigación relacionados al tema, conjuntado como un sistema integral que muestra gráficamente y describe los bloques de información que deben considerarse. Como un caso particular de estudio el modelo se aplica en Nuevo Laredo, para demostrar que existen las condiciones necesarias para instalar sistemas fotovoltaicos. Se considera la medición de variables in situ mediante instrumentos especiales y las obtenidas de bases de datos o software especial, se analizan y se comparan con normas, especificaciones de fabricantes, regulaciones y parámetros de referencia, lo que permite determinar la viabilidad de la región para instalar sistemas solares fotovoltaicos. Finalmente, la aplicación del modelo requiere elaborar un reporte técnico de los resultados obtenidos. metadata Cruz Arellano, Martin y Castillo Tellez, Margarita mail SIN ESPECIFICAR (2021) Planteamiento de un modelo energético descriptivo aplicable a la instalación de sistemas solares fotovoltaicos interconectados a la red mediante generación distribuida: caso de estudio en Nuevo Laredo. Project Design and Management, 3 (1). pp. 112-137. ISSN 2683-1597
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El consumo de energía a nivel mundial continúa incrementándose debido al aumento demográfico y desarrollo tecnológico, sin embargo, el 73 % de la energía utilizada proviene de fuentes fósiles altamente contaminantes para el planeta y cuyas reservas mundiales se reducen aceleradamente, utilizando únicamente un porcentaje menor de energías limpias o renovables que mitiguen el calentamiento global, el cambio climático y aseguren la sustentabilidad energética mundial. Conscientes de esta problemática nacional y mundial, se propone un modelo energético descriptivo que incluya metodológicamente los pasos a seguir para determinar la viabilidad de instalar sistemas solares fotovoltaicos en cualquier región del mundo, mediante el análisis del recurso energético renovable disponible, de las variables medioambientales y eléctricas y, de los recursos humanos, materiales y financieros. El modelo propuesto se desarrolla y diseña mediante la recopilación, integración y análisis de diversas fuentes y trabajos de investigación relacionados al tema, conjuntado como un sistema integral que muestra gráficamente y describe los bloques de información que deben considerarse. Como un caso particular de estudio el modelo se aplica en Nuevo Laredo, para demostrar que existen las condiciones necesarias para instalar sistemas fotovoltaicos. Se considera la medición de variables in situ mediante instrumentos especiales y las obtenidas de bases de datos o software especial, se analizan y se comparan con normas, especificaciones de fabricantes, regulaciones y parámetros de referencia, lo que permite determinar la viabilidad de la región para instalar sistemas solares fotovoltaicos. Finalmente, la aplicación del modelo requiere elaborar un reporte técnico de los resultados obtenidos.
| Tipo de Documento: | Artículo |
|---|---|
| Palabras Clave: | Sistema fotovoltaico, generación distribuida, modelo energético, radiación solar, variables meteorológicas |
| Clasificación temática: | Materias > Ingeniería |
| Divisiones: | Universidad Internacional Iberoamericana México > Investigación > Artículos y libros |
| Depositado: | 07 Jul 2022 23:30 |
| Ultima Modificación: | 31 Oct 2022 23:30 |
| URI: | https://repositorio.unini.edu.mx/id/eprint/2605 |
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Single-cell omics for nutrition research: an emerging opportunity for human-centric investigations
Understanding how dietary compounds affect human health is challenged by their molecular complexity and cell-type–specific effects. Conventional multi-cell type (bulk) analyses obscure cellular heterogeneity, while animal and standard in vitro models often fail to replicate human physiology. Single-cell omics technologies—such as single-cell RNA sequencing, as well as single-cell–resolved proteomic and metabolomic approaches—enable high-resolution investigation of nutrient–cell interactions and reveal mechanisms at a single-cell resolution. When combined with advanced human-derived in vitro systems like organoids and organ-on-chip platforms, they support mechanistic studies in physiologically relevant contexts. This review outlines emerging applications of single-cell omics in nutrition research, emphasizing their potential to uncover cell-specific dietary responses, identify nutrient-sensitive pathways, and capture interindividual variability. It also discusses key challenges—including technical limitations, model selection, and institutional biases—and identifies strategic directions to facilitate broader adoption in the field. Collectively, single-cell omics offer a transformative framework to advance human-centric nutrition research.
Manuela Cassotta mail manucassotta@gmail.com, Yasmany Armas Diaz mail , Danila Cianciosi mail , Bei Yang mail , Zexiu Qi mail , Ge Chen mail , Santos Gracia Villar mail santos.gracia@uneatlantico.es, Luis Alonso Dzul López mail luis.dzul@uneatlantico.es, Giuseppe Grosso mail , José L. Quiles mail , Jianbo Xiao mail , Maurizio Battino mail maurizio.battino@uneatlantico.es, Francesca Giampieri mail francesca.giampieri@uneatlantico.es,
Cassotta
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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 ,
Sharobiddinov
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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 ,
Saleem
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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 ,
Saleem
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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 ,
Rosi