Nowadays, image analysis has a relevant role in most scientific and research areas. This process is used to extract and understand information from images to obtain a model, knowledge, and rules in the decision process. In the case of biological areas, images are acquired to describe the behavior of a biological agent in time such as cells using a mathematical and computational approach to generate a system with automatic control. In this paper, MCF7 cells are used to model their growth and death when they have been injected with a drug. These mammalian cells allow understanding of behavior, gene expression, and drug resistance to breast cancer. For this, an automatic segmentation method called GEMA is presented to analyze the apoptosis and confluence stages of culture by measuring the increase or decrease of the image area occupied by cells in microfluidic devices. In vitro, the biological experiments can be analyzed through a sequence of images taken at specific intervals of time. To automate the image segmentation, the proposed algorithm is based on a Gabor filter, a coefficient of variation (CV), and linear regression. This allows the processing of images in real time during the evolution of biological experiments. Moreover, GEMA has been compared with another three representative methods such as gold standard (manual segmentation), morphological gradient, and a semi-automatic algorithm using FIJI. The experiments show promising results, due to the proposed algorithm achieving an accuracy above 90% and a lower computation time because it requires on average 1 s to process each image. This makes it suitable for image-based real-time automatization of biological lab-on-a-chip experiments.

On the morning of 7 March 2025, the Argentine district of Bahía Blanca experienced a severe flooding that led to at least 15 fatalities. This study presents the main aspects of the event based on different data sources that helped to explain the exceptional precipitation of about 300 mm and rapid flooding. The results indicated that Bahía Blanca district presented flooded areas of approximately 33 km² (1.4% of the total area) on 10 March, most of them concentrated in the non-urbanized zones. However, a total of 18 km² (0.8% of the total area) was still identified on 11 March, with a greater impact on the low-lying urban areas of the Bahía Blanca, General Daniel Cerri, and Ingeniero White towns. The likelihood of severe weather development was confirmed from instability indices. The very high moisture content along a low-level convergence line, jointly with upper-level divergence, contributed to deep convective cloud development that affected Bahía Blanca for at least 6 h. Increasing knowledge of urban floods from different data sources can support weather forecasts to provide timely warnings, essential to mitigate the adverse impacts of these extreme weather events on low-lying urban areas.

Coastal regions and surface waters are among the fundamental biological and social development resources worldwide. For this reason, it is essential to thoroughly monitor these regions to determine and characterize their geographical features and environmental health. These geographical regions, however, present several monitoring challenges when using remotely sensed imagery. Small water bodies tend to be surrounded by swamps, marshes, or vegetation, making accurate border detection difficult. Coastal waters, in turn, experience several phenomena due to winds, undercurrents, and waves, which also hamper the detection of environmental hazards like oil spills. In this work, we propose an automated segmentation algorithm that can be applied to these targets in airborne and spaceborne SAR images. The method is based on pointwise detection in fuzzy borders using a parameter estimation of the ${\mathcal{G}}^0$ distribution, which has been successfully used in similar contexts. The underlying assumption is that the sought-for border separates regions with different textures, each having different distribution parameters. Then, stochastic distances can identify the most likely point where this parameter change occurs. A curve interpolation algorithm then estimates the actual contour of the body given the detected points. We assess the adequacy of eight stochastic distances that are mostly applied in the literature. We evaluate the performance of our method in terms of similarity between true and detected boundaries on simulated and actual SAR images, achieving promising results. The performance of our proposal is assessed by Hausdorff distance and Intersection over Union. In the case of synthetic data, the selection of the best stochastic distance depends on the parameters of the ${\mathcal{G}}_I^0$ distribution. In contrast, the harmonic-mean and triangular distances produced the best results in detecting borders in three actual SAR images of lagoons. Finally, we present the results of our proposal applied to an image with oil spills using Bhattacharyya, Hellinger, and Jensen–Shannon distances.

El conocimiento del potencial de escorrentía de una cuenca resulta fundamental para la gestión hídrica a corto y largo plazo y la toma de decisiones socio-políticas y ambientales. La simplicidad del modelo NCRS-CN lo convierte en una herramienta útil en cuencas con escasos registros hidrológicos continuos. La validación y ajuste de los CN en cuencas instrumentadas imprime fiabilidad y transferibilidad al método. Se obtuvieron valores de CN por ponderación espacial, CNc, y por análisis de evento-respuesta, métodos SII y asintótico. Los CN se validaron sobre series de precipitación-escorrentía históricas por evento (corto plazo) y acumuladas (largo plazo) para dos subcuencas serranas con diversidad areal, B1 y B2, del arroyo Napostá Grande, Argentina, evaluando finalmente un ajuste a la realidad hidrológica de la cuenca. El método SII arroja mejores resultados que el asintótico y que el CNc para análisis de corto plazo en ambas subcuencas, mientras que para análisis de láminas acumuladas a largo plazo, anuales o plurianuales, resulta más representativo el método ajustado para ambas, B1 y B2. Los modelos analizados resultan una representación matemática determinística que permitiría predecir la escorrentía a partir de datos de precipitación, pudiendo extrapolarse su uso a cuencas regionales no instrumentadas con diversidad areal.

Form and shape descriptors are among the most useful features for object identification and recognition. Even though there exists a widely used set of shape descriptors and underlying computational methods for their evaluation, frequently they fail to distinguish among very similar objects that they appear very different to the human eye. The authors propose a new set of shape descriptors based on a finer determination of the object symmetry axes, and a more accurate estimation of the bilateral and radial symmetries. These descriptors were thoroughly tested using several synthetic and real objects with varying degrees of symmetry. The methods for axes estimation and symmetry descriptors extraction outperform the widespread shape descriptors in recognising and identifying among very similar objects.