Modelo de comunicación efectiva para la difusión de los programas y proyectos de inversión pública del Departamento de Loreto, Perú
Artículo
Materias > Comunicación
Universidad Europea del Atlántico > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Artículos y libros
Abierto
Inglés, Español
Esta investigación ha sido desarrollada con el objetivo general de determinar un modelo de comunicación efectiva para la difusión de los Programas y Proyectos de Inversión Pública (PIP) del Departamento de Loreto, que ocupa la tercera parte del territorio del Perú, y, dadas sus características geográficas, existe mucha influencia cultural de Colombia y Brasil. Desde la perspectiva metodológica, se basó en un enfoque cuantitativo, de nivel descriptivo, con un diseño de campo, no experimental, transversal, que se apoyó en encuestas aplicadas a los tenientes gobernadores de los poblados ubicados en las fronteras con Colombia y Brasil. Una vez desarrollado el trabajo de campo, se realizó el procesamiento de la información, generando así el análisis descriptivo, la discusión de los resultados y la propuesta de modelo. En esencia, se llegó a la conclusión de que existen importantes limitaciones en el modelo actual de difusión de los PIP en el Departamento de Loreto, debilidades concernientes a todos los elementos de la comunicación: emisores dispersos y no preparados, receptores no caracterizados, canales desaprovechados, mensajes no codificados ni contextualizados, retroalimentación no estimulada. En vista de lo cual se diseña un Modelo de Comunicación Efectiva para la Difusión de los PIP (MCE-D-PIP) que plantea el desarrollo de una Sala Situacional de Comunicación Efectiva (SSCE– PIP), que permita potenciar los roles de productores, consumidores y prosumidores de la información, mediante la diversificación de los canales y una especializada codificación del mensaje, en función del contexto: diversidad cultural, condiciones educativas, factores tecnológicos, entre otros.
metadata
Gallo Infantes, Francisco Antonio; Arambarri, Jon; Lloret Romero, Nuria y Cadillo López, Claudet
mail
SIN ESPECIFICAR, jon.arambarri@uneatlantico.es, SIN ESPECIFICAR, SIN ESPECIFICAR
(2023)
Modelo de comunicación efectiva para la difusión de los programas y proyectos de inversión pública del Departamento de Loreto, Perú.
MLS Educational Research, 7 (1).
ISSN 2792-9280
Resumen
Esta investigación ha sido desarrollada con el objetivo general de determinar un modelo de comunicación efectiva para la difusión de los Programas y Proyectos de Inversión Pública (PIP) del Departamento de Loreto, que ocupa la tercera parte del territorio del Perú, y, dadas sus características geográficas, existe mucha influencia cultural de Colombia y Brasil. Desde la perspectiva metodológica, se basó en un enfoque cuantitativo, de nivel descriptivo, con un diseño de campo, no experimental, transversal, que se apoyó en encuestas aplicadas a los tenientes gobernadores de los poblados ubicados en las fronteras con Colombia y Brasil. Una vez desarrollado el trabajo de campo, se realizó el procesamiento de la información, generando así el análisis descriptivo, la discusión de los resultados y la propuesta de modelo. En esencia, se llegó a la conclusión de que existen importantes limitaciones en el modelo actual de difusión de los PIP en el Departamento de Loreto, debilidades concernientes a todos los elementos de la comunicación: emisores dispersos y no preparados, receptores no caracterizados, canales desaprovechados, mensajes no codificados ni contextualizados, retroalimentación no estimulada. En vista de lo cual se diseña un Modelo de Comunicación Efectiva para la Difusión de los PIP (MCE-D-PIP) que plantea el desarrollo de una Sala Situacional de Comunicación Efectiva (SSCE– PIP), que permita potenciar los roles de productores, consumidores y prosumidores de la información, mediante la diversificación de los canales y una especializada codificación del mensaje, en función del contexto: diversidad cultural, condiciones educativas, factores tecnológicos, entre otros.
| Tipo de Documento: | Artículo |
|---|---|
| Palabras Clave: | Comunicación efectiva, difusión, programas y proyectos, inversión pública, modelos de comunicación |
| Clasificación temática: | Materias > Comunicación |
| Divisiones: | Universidad Europea del Atlántico > Investigación > Producción Científica Universidad Internacional Iberoamericana México > Investigación > Artículos y libros |
| Depositado: | 15 Jul 2022 23:30 |
| Ultima Modificación: | 21 Oct 2024 23:30 |
| URI: | https://repositorio.unini.edu.mx/id/eprint/2844 |
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The proliferation of damaging content on social media in today’s digital environment has increased the need for efficient hate speech identification systems. A thorough examination of hate speech detection methods in a variety of settings, such as code-mixed, multilingual, visual, audio, and textual scenarios, is presented in this paper. Unlike previous research focusing on single modalities, our study thoroughly examines hate speech identification across multiple forms. We classify the numerous types of hate speech, showing how it appears on different platforms and emphasizing the unique difficulties in multi-modal and multilingual settings. We fill research gaps by assessing a variety of methods, including deep learning, machine learning, and natural language processing, especially for complicated data like code-mixed and cross-lingual text. Additionally, we offer key technique comparisons, suggesting future research avenues that prioritize multi-modal analysis and ethical data handling, while acknowledging its benefits and drawbacks. This study attempts to promote scholarly research and real-world applications on social media platforms by acting as an essential resource for improving hate speech identification across various data sources.
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Accurate solar and photovoltaic (PV) power forecasting is essential for optimizing grid integration, managing energy storage, and maximizing the efficiency of solar power systems. Deep learning (DL) models have shown promise in this area due to their ability to learn complex, non-linear relationships within large datasets. This study presents a systematic literature review (SLR) of deep learning applications for solar PV forecasting, addressing a gap in the existing literature, which often focuses on traditional ML or broader renewable energy applications. This review specifically aims to identify the DL architectures employed, preprocessing and feature engineering techniques used, the input features leveraged, evaluation metrics applied, and the persistent challenges in this field. Through a rigorous analysis of 26 selected papers from an initial set of 155 articles retrieved from the Web of Science database, we found that Long Short-Term Memory (LSTM) networks were the most frequently used algorithm (appearing in 32.69% of the papers), closely followed by Convolutional Neural Networks (CNNs) at 28.85%. Furthermore, Wavelet Transform (WT) was found to be the most prominent data decomposition technique, while Pearson Correlation was the most used for feature selection. We also found that ambient temperature, pressure, and humidity are the most common input features. Our systematic evaluation provides critical insights into state-of-the-art DL-based solar forecasting and identifies key areas for upcoming research. Future research should prioritize the development of more robust and interpretable models, as well as explore the integration of multi-source data to further enhance forecasting accuracy. Such advancements are crucial for the effective integration of solar energy into future power grids.
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