Sistema Regional de Información
en línea para Revistas Científicas de América Latina,
el Caribe, España y Portugal

In a competitive media environment, news outlets face the challenge of adopting new technologies to remain relevant. This study explores the use of Artificial Intelligence (AI) in content generation for visual platforms, with a specific focus on Instagram, and analyzes how digital marketing strategies can facilitate this transition. The main objective is to identify key strategies to effectively integrate AI-generated content on Instagram, enhancing engagement and content personalization. To achieve this, a mixed methodology was employed, combining a bibliographic review of AI and digital marketing with a case study of the Guayaquil-based digital outlet Punto y Coma. The findings reveal that precise audience segmentation, predictive analysis, and the use of AI tools to create visually appealing content are effective strategies to maximize the relevance and impact of posts. In the case of Punto y Coma, the implementation of an AI-generated avatar as a news presenter significantly increased engagement on Instagram, boosting both reach and audience loyalty. The study also identifies key challenges, such as organizational resistance to change, training in modern technologies, and the need to humanize AI-generated content to gain public trust. In conclusion, adopting AI on visual platforms is a powerful tool for news outlets, provided it is implemented with clear strategies tailored to market needs.

Artificial intelligence (AI) represents a gateway to possibilities that optimizes teaching work by facilitating personalized learning and improving efficiency in daily planning. However, its use in education remains limited due to factors such as lack of awareness, resistance to change, and technological limitations, which generate a high cognitive load for teachers. This mixed-method study combines teacher surveys and bibliographic analysis to identify AI tools applicable to the creation of teaching resources. The results reveal that, although 93.3% of teachers recognize the usefulness of AI in education, only 33.3% use it frequently. Based on these findings, strategies are proposed to promote its adoption, including identifying appropriate programs and developing a training plan. The objective of the research is to effectively integrate AI into teaching practice, ensuring that teachers at the María Andrea Private Educational Unit can make the most of the technology to enrich their classes and improve their students' learning experience.

Radio occultation (RO) has been used for more than six decades to probe the atmosphere of other planets. The advent of Global Navigation Satellite Systems (GNSS) and low-orbit satellite missions has opened up new possibilities for investigating the Earth’s atmosphere, including the use of GNSS-RO. The influence of the atmosphere on the signal transmitted by GNSS allows the retrieval of atmospheric information. However, the Abel inverse, commonly used to retrieve such profiles, has its performance degraded in some cases due to the imposition of the condition of spherical symmetry of the atmosphere. In order to overcome this limitation, several studies have proposed alternative approaches, among which the use of the Abel inverse aided by ionospheric products has shown promising results. However, some limitations have been observed in regions with greater variability in electron distribution, as is the case in Brazil. In this context, the aim of this research is to develop a methodology for retrieving electron density profiles using RO data, using Abel’s inverse with aided by ionospheric maps. The methodology was implemented in the software used for one of today’s main RO missions. The study proposed various methodologies for evaluating ionospheric maps, considering regional, global and hybrid products. In general, two global products showed the best results, so the methodologies for retrieving RO profiles are tested using such global maps. The evaluations considered: in situ measurements, where the best performing products varies according to the scenario considered; and ionosonde data, where the product derived from the proposed methodology perform best.

During the last decade, the municipality of Guadalajara, Jalisco (Mexico) faces the challenges of an accelerated and dispersed urbanization, which highlights the strong demand for affordable housing that guarantees the well-being of the population that lives in the city. However, the municipality lacks an objective assessment criterion. Reason why the research focuses on perfoming classic statistical and geostatistical analysis to evaluate the local real estate market. The application of statistics in the field of real estate valuation aims to analyze data from a representative sample of homes in the municipality that allows explaining their correlations and dependencies in order to estimate their market value. Therefore, the valuation analysis of private homes in the municipality of Guadalajara is based on the compilation and homogenization of the spatial information available from the Cadastre and the National Institute of Statistics and Geography (INEGI) categorizing variables, at block scale, that influence the price of homes. After obtaining these variables, two statistical techniques were used for their analysis. The Multiple Linear Regression Model of the data that identifies the variables that influence the value of the property, allowing us to understand the relationship between different variables and the real state value, as well as predict or estimate housing prices. The second technique applied was the spatial statistical analysis using Ordinary Kriging method, which made it possible to estimate unknown or unsampled values, considering not only the proximity of the known points, but also the spatial relationship between them, generating a map of the estimates of the real state values throughout the study area. The combination of these methods allows us to comprehensively address the complexity of the spatial and multivariate relationships of the real state market in the study area.

Deforestation and forest degradation are two global change drivers that contribute to greenhouse gases emissions, biodiversity loss and reduction in the quality of ecosystemic services. Due to their ability to study large extents and count with a historical record, remote sensing has demonstrated being an essential tool to monitor these processes. Besides, the development of new methods of analysis, such as deep learning and images with higher spatial, spectral and temporal resolution open the possibility to develop methods that enhance the current monitoring capacities. In this context, the current study evaluated the performance of deep learning algorithms with multispectral and synthetic aperture radar images to classify different land use land covers, identify deforestation and forest degradation. The results show that, in general, deep learning algorithms obtain more accurate results than its machine learning counterparts, due to the incorporation of spatial and temporal dimensions. Nonetheless, this performance is conditioned by sample size and the force of the relation between the remote signal and the class or attribute being evaluated. On the other hand, the combination of multispectral and synthetic aperture radar was, in general, beneficial; although in certain cases, it did not add new useful information, in comparison with the one already contributed by the multispectral ones. The results indicate that these techniques are capable of obtaining more accurate evaluations to identify deforestation and forest degradation; thus, they represent a very attractive alternative to develop future tools to monitor these processes.

  Studying the territorial dynamics of the municipality of Catu-Bahia, Brazil, using maps and other documentary sources, applying geoprocessing techniques in Geographic Information Systems (GIS), comprised the objective of this research. As chronological landmarks, the elevation of Catu to town in the 19th century until the 20th century was considered, namely in 1967, the year in which the first systematic topographic mapping covering the municipality was carried out. The study resulting from Historical Cartography, the use of GIS tools and the geospatial data library, made it possible to visualize the changes in the structure of the municipality regarding the promotion of the town to city and villages to settlement, the advent of new locations, the implementation and/or improvement of circulation routes, the identification of buildings that remained in the territory, the emergence of neighborhoods and the installation of streets in the municipal headquarters, in addition to the verification of old sugar mills, farms and other lands that spanned the century and had to adapt to the new local economic reality, fueled by the decline in the production of sugar and other sugarcane derivatives and, later, by the discovery of oil. The changes in the municipal limit of Catu, due to the territorial dismemberment to create the municipality of Pojuca, and the perimeter configuration after the discovery of oil, were also identified from spatial analyzes in GIS. The toponymic signs indicated marks of social groups that lived in Catu, or passed through there and the Portuguese influence was present in the majority of toponyms in the three cartographic documents analyzed, followed by indigenous and African denominations. In Portuguese language toponyms, religious symbols marked the largest number of denominations, especially those of mills and farms. However, in the general total of toponyms, the motivation in vegetables (Phytotoponyms) was the most recurrent.

The study and dynamics of urban space leads to thinking about processes that occur in the territory and generate impacts on the configuration and development of cities. These processes, whose interrelations constitute the structure of a system that functions as an organized whole, redefinethe society nature dialectic of a complex system. Risk management constitutes an equation between the concepts of threat and vulnerability that implies the analysis of the different dimensions of the phenomena. The proposed methodology aims to be a guide to understand the risk in medium-sized cities for the management of floods in urban basins. This involves considering the set of elements that intervene in the configuration and dynamics of the territory. Each process defines a subsystem within the whole determined by the natural subsystem, the political-institutional, the economic and the social delineated by its own dimensions and variables. The objective of this work is to build a two-dimensional hydrological model to generate runoff and surface drainage scenarios within the urban basin of the city of Tandil, detecting flooded and inundated areas during and after a hydrometeorological event. The model will be defined from a Digital Elevation Model (DEM), where the topography of the terrain will have its own parameters for simulating the surface runoff process, taking into account hydrological, physical and urban variables. The area flooded in 2011 was analyzed by extracting the product of the velocity and depth maps of surface flooding to identify the areas of real and potential impact by correlating physical and urban variables, identifying the spatial distribution patterns of the flooded area and quantifying the hazard based on the determination of a technical scale used by the Australian Institute for Disaster Resilience (AEMI).

Adequate management of areas facing possible landslides is of utmost importance in several Latin American countries. It is important to point out that coordination between scientists, politicians and the population in general is extremely significant, since the coordination between the three aforementioned entities will have to be permanent in order to restructure urban areas with certain levels of risk. The methodology used in this study focused on mapping the main urban areas by means of geoinformatics techniques using geomatics through Geographic Information Systems (GIS), therefore, the analysis of raster images, vectorial, Lidar points and photogrammetry from drones was performed, ratifying or rectifying the data in the field, together with the support of journalistic news of the main existing media in Mexico City (CDMX). The final result of these geoinformatic methodologies applied to urban areas in the face of possible landslides forged precise mapping on which possible urban restructuring should be instituted in areas of high and very high risk; always with elements of consultation and reflection directed to the inhabitants through permanent dialogue with specialists in behavioral issues such as psychologists, sociologists and social workers, to urbanistically reestablish the population in areas with geomorphological characteristics of low or no urban risk.