Floods are the most common phenomenon and cause the most significant economic and social damage to the population. They are becoming more frequent and dangerous. Consequently, it is necessary to create strategies to intervene effectively in the mitigation and resilience of the affected areas. Different methods and techniques have been developed to mitigate the damage caused by this phenomenon. Satellite programs provide a large amount of data on the earth's surface, and geospatial information processing tools help manage different natural disasters. Likewise, deep learning is an approach capable of forecasting time series that can be applied to satellite images for flood prediction and mapping. This article presents an approach for flood segmentation and visualization using the U-Net architecture and Sentinel-1 synthetic aperture radar (SAR) satellite imagery. The U-Net architecture can capture relevant features in SAR images. The approach comprises various phases, from data loading and preprocessing to flood inference and visualization. For the study, the georeferenced dataset Sen1Floods11 is used to train and validate the model through different epochs and training. A study area in southeastern Mexico that presents frequent floods was chosen. The results demonstrate that the segmentation model achieves high accuracy in detecting flooded areas, with promising metrics regarding loss, precision, and F1-score.

One of the main challenges in the efficient operation of a high-performance computing (HPC) center is the energy consumption generated by the operation of the data center where the HPC equipment is housed, mainly because this consumption is reflected in very high accounts payable, and this may affect the level of service offered to users. The study of the different factors and elements that can make energy consumption more efficient in these data centers provides an opportunity to focus these resources on elements that favor the use of HPC. The design variables provided by manufacturers to manage HPC systems and monitoring systems provide an accurate view of the behavior of these variables according to how they are used. HPC architectures are configured in a very particular way for each HPC data center, creating particular scenarios of operation and performance in each implementation. Various proposals and technologies have been developed for the analysis of the energy consumption of a data center, and the processing elements include a series of indicators and technologies that manufacturers have developed to determine the energy efficiency. This article seeks to identify this series of processing and performance variables, which affect the energy consumption of HPC equipment, for the implemented computing architectures based on the analysis of performance models to obtain a general over-view of their effect on energy consumption in a case study to identify the behaviors of particular job assignment factors and provide an analysis of the energy consumption under particular conditions.

Cardiovascular diseases stand as the leading cause of mortality worldwide, underscoring the urgent need for effective tools that enable early detection and monitoring of at-risk patients. This study combines Artificial Intelligence (AI) techniques—specifically the k-means clustering algorithm—alongside dimensionality reduction methods like Principal Component Analysis (PCA) and Uniform Manifold Approximation and Projection (UMAP) to identify patient groups with varying levels of heart attack risk. We used a publicly available clinical dataset with 1319 patient records, which included variables such as age, gender, blood pressure, glucose levels, CK-MB Creatine Kinase MB (KCM), and troponin levels. We normalized and prepared the data, then we employed PCA and UMAP to reduce dimensionality and facilitate visualization. Using the k-means algorithm, we segmented the patients into distinct groups based on their clinical features. Our analysis revealed two distinct patient groups. Group 2 exhibited significantly higher levels of troponin (mean 0.4761 ng/mL), KCM (18.65 ng/mL), and glucose (mean 148.19 mg/dL) and was predominantly composed of men (97%). These factors indicate an increased risk of cardiac events compared to Group 1, which had lower levels of these biomarkers and a slightly higher average age. Interestingly, no significant differences in blood pressure were observed between the groups. This study demonstrates the effectiveness of combining Machine Learning (ML) techniques with dimensionality reduction methods to enhance risk stratification accuracy in cardiology. By enabling more targeted interventions for high-risk patients, our unsupervised segmentation approach focuses on intrinsic data patterns rather than predefined diagnostic labels, serves as a powerful complement to traditional risk assessment tools.

La transformación digital contemporánea ha reconfigurado de manera estructural las condiciones ontológicas, epistemológicas, identitarias, emocionales y psicopolíticas de la existencia humana. Tecnologías como la inteligencia artificial, el big data, el metaverso, las plataformas digitales y los entornos inmersivos modifican los modos de ser, conocer, aprender, relacionarse y participar socialmente, evidenciando que las nociones tradicionales de desarrollo humano son insuficientes para comprender las dinámicas de la era algorítmica. El objetivo de este trabajo es fundamentar teórica y conceptualmente el desarrollo humano digital como un nuevo paradigma interpretativo, capaz de dar respuesta a dichas transformaciones. Para ello, la estrategia metodológica se basó en un análisis documental amplio y multidisciplinar, que permitió sintetizar aportes provenientes de las ciencias sociales, filosofía, sicología, educación y estudios sobre tecnología. Como principales hallazgos se identifican 7 dimensiones fundamentales ontológica, epistemológica, identitaria, psicoemocional, ética, sociotécnica y agencia crítica que sustentan la necesidad de un nuevo marco conceptual. A partir de ello, se propone y se fundamenta el concepto de[1] desarrollo humano digital, entendido como el proceso mediante el cual las personas expanden sus capacidades, libertades, agencia crítica y bienestar en ecologías sociotécnicas híbridas, físicas, simbólicas y digitales mediante la integración consciente, ética y emocionalmente regulada de tecnologías y algoritmos. Esta propuesta ofrece una visión integral y holística orientada a comprender, atender y fortalecer el florecimiento humano en contextos altamente digitalizados.

Cardiovascular diseases stand as the leading cause of mortality worldwide, underscoring the urgent need for effective tools that enable early detection and monitoring of at-risk patients. This study combines Artificial Intelligence (AI) techniques—specifically K-means clustering algorithm—alongside dimensionality reduction methods like Principal Component Analysis (PCA) and Uniform Manifold Approximation and Projection (UMAP) to identify patient groups with varying levels of heart attack risk. We were using a publicly available clinical dataset with 1319 patient records, which included variables such as age, gender, blood pressure, glucose levels, KCM, and troponin levels. We normalized and prepared the data, then we employed PCA and UMAP to reduce dimensionality and facilitate visualization. Using the K-means algorithm, we segmented the patients into distinct groups based on their clinical features. Our analysis revealed two distinct patient groups. Group 2 exhibited significantly higher levels of troponin (mean 0.4761 ng/mL), KCM (18.65 ng/ml) and glucose (mean 148.19 mg/dL) and was predominantly composed of men (97%). These factors indicate an increased risk of cardiac events compared to Group 1, which had lower levels of these biomarkers and a slightly higher average age. Interestingly, no significant differences in blood pressure were observed between the groups. This study demonstrates the effectiveness of combining Machine Learning (ML) techniques with dimensionality reduction methods to enhance risk stratification accuracy in cardiology. By enabling more targeted interventions for high-risk patients, our approach contributes to improved prevention strategies.