Universidad Tecnológica de Bolívar
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Density of the level sets of the metric mean dimension for homeomorphisms
No full textLet N be an n-dimensional compact riemannian manifold, with n ≥ 2. In this paper, we prove that for any α ∈ [0, n], the set consisting of homeomorphisms on N with lower and upper metric mean dimensions equal to α is dense in Hom(N). More generally, given α, β ∈ [0, n], with α ≤ β, we show the set consisting of homeomorphisms on N with lower metric mean dimension equal to α and upper metric mean dimension equal to β is dense in Hom(N). Furthermore, we also give a proof that the set of homeomorphisms withupper metric mean dimension equal to n is residual in Hom(N)
Machine learning models for predicting geomagnetic storms across five solar cycles using Dst index and heliospheric variables
No full textThis study aims to improve the understanding of geomagnetic storms by utilizing machine learning models and analyzing several heliophysical variables, such as the interplanetary magnetic field, proton density, solar wind speed, and proton temperature. Rather than relying on traditional correlation-based methods, we employ advanced machine learning techniques to examine the complex relationships between these factors and geomagnetic storms. Our analysis covers a large dataset spanning six solar cycles, including the current 25th cycle, to provide comprehensive insights into the dynamics of these storms.
Our study highlights the significance of the interplanetary magnetic field as a key predictor of geomagnetic storms, challenging previous beliefs that primarily focused on sunspot activity. By using high-resolution data, we uncover new patterns and provide a more detailed analysis of the factors influencing geomagnetic storms. We emphasize the importance of considering a range of heliophysical variables, such as proton temperature and flow pressure, which offer new insights into the complex dynamics driving these storm events.
The application of machine learning models, particularly Random Forest and Gradient Boosting, demonstrated superior predictive accuracy compared to traditional methods. Our results reveal that the Dst-index MIN, scalar B, and alpha/proton ratio are among the most influential factors, accounting for a significant portion of the prediction model’s accuracy. These findings underscore the utility of machine learning in identifying critical drivers of geomagnetic activity and enhancing forecast precision.
Additionally, our research underscores the need for comprehensive models that can accurately predict geomagnetic storms by integrating various data sources. This machine learning approach not only improves predictive accuracy but also enhances our understanding of the underlying mechanisms of space weather. The insights gained from this study have important implications for both scientific research and practical applications, such as improving early warning systems for geomagnetic storms and mitigating their potential impacts on Earth
Exploring the determinants of happiness in Mexico: The interplay of social networks, psychological well-being, and socioeconomic factors
No full textOver recent years, there has been significant growth in research on happiness. It is essential to understand the factors that affect people's well-being to develop effective government policies that aim to improve the quality of life for citizens in Mexico. Unfortunately, this subject has been under-explored, especially in the Mexican context, with limited studies focusing on the topic. This study aims to comprehensively review the current literature on happiness, social networking, psychological factors, and socioeconomic factors to identify the critical variables associated with the happiness of Mexican citizens. Further, based on data from Mexico's 2021 National Survey of Self-reported Well-being (ENBIARE), we conducted a rigorous examination of this dataset to identify the principal factors that impact the well-being of Mexican citizens, employing both exploratory and confirmatory factor analysis
Manifestações e situações de violência escolar percebida nas comunidades educativas de Cartagena, Colômbia
No full textGran parte de los colegios públicos de Cartagena (Colombia) se ubican en
zonas vulnerables de esta ciudad, caracterizándose estos colegios por la
elevada incidencia de la violencia, viéndose la calidad de la educación, la
convivencia y el bienestar emocional de la comunidad educativa afectada de
forma grave. Este trabajo tuvo como objetivo analizar percepciones sobre la
violencia escolar, a partir de relatos de 50 participantes: estudiantes, madres
y docentes de instituciones educativas que viven en zonas vulnerables de
Cartagena de Indias. De esta manera se pretende abordar los testimonios
sobre la violencia escolar de los diferentes agentes sociales entrevistados
con el objetivo de construir soluciones conjuntas desde la escuela y la comunidad.
Los resultados indican que la violencia escolar se percibe en función
de una diversidad de manifestaciones que ocurren y se entrecruzan en tres
grandes escenarios: escolar, familiar y socio-comunitario. Es percibida como
un juego, producto de tensiones entre familia y escuela. Se le asocia con un
déficit de competencias sociales hasta vincularla con problemáticas como la
drogadicción o la carencia de oportunidades para acceder a un trabajo digno.
Estos resultados aportan a las escuelas insumos para el diseño e implementación
de programas y estrategias de prevención contra la violencia escolar
Multitemporal Analysis of Inland Water Bodies in the Context of Water Security: A Colombian Case Study
No full textThis paper presents an analysis of two inland water bodies, Salvajina Reservoir and Sonso’s lagoon, located in the Upper Cauca River Basin, Colombia. Such analysis is carried out in the context of Water Security (WS) by considering their inherent problems to be monitored in a permanent manner (i.e., difficult access, insecurity) and taking advantage of Remote Sensing (RS) platforms. To do so, temporal mapping was done by: (i) automatically segmenting the inland water bodies; (ii) applying the Case 2 Regional Coast Color (C2RCC) algorithm to obtain an approximation to water quality parameters (e.g., chl-a, TSM); (iii) extracting statistical information such as area variation, radiometric index values, and mean values in parameters of water quality; and (iv) providing relevant information for decision makers in the context of WS. The analysis was done over Landsat-8 and Sentinel-2 images between 2014-2021 and 2020-2021, respectively. Planet images were used to validate the segmentation results
Assessing Fuchs Corneal Endothelial Dystrophy Using Artificial Intelligence-Derived Morphometric Parameters From Specular Microscopy Images
No full textPurpose:
The aim of this study was to evaluate the efficacy of artificial intelligence–derived morphometric parameters in characterizing Fuchs corneal endothelial dystrophy (FECD) from specular microscopy images.
Methods:
This cross-sectional study recruited patients diagnosed with FECD, who underwent ophthalmologic evaluations, including slit-lamp examinations and corneal endothelial assessments using specular microscopy. The modified Krachmer grading scale was used for clinical FECD classification. The images were processed using a convolutional neural network for segmentation and morphometric parameter estimation, including effective endothelial cell density, guttae area ratio, coefficient of variation of size, and hexagonality. A mixed-effects model was used to assess relationships between the FECD clinical classification and measured parameters.
Results:
Of 52 patients (104 eyes) recruited, 76 eyes were analyzed because of the exclusion of 26 eyes for poor quality retroillumination photographs. The study revealed significant discrepancies between artificial intelligence–based and built-in microscope software cell density measurements (1322 ± 489 cells/mm2 vs. 2216 ± 509 cells/mm2, P < 0.001). In the central region, guttae area ratio showed the strongest correlation with modified Krachmer grades (0.60, P < 0.001). In peripheral areas, only guttae area ratio in the inferior region exhibited a marginally significant positive correlation (0.29, P < 0.05).
Conclusions:
This study confirms the utility of CNNs for precise FECD evaluation through specular microscopy. Guttae area ratio emerges as a compelling morphometric parameter aligning closely with modified Krachmer clinical grading. These findings set the stage for future large-scale studies, with potential applications in the assessment of irreversible corneal edema risk after phacoemulsification in FECD patients, as well as in monitoring novel FECD therapies
Engineering the Future: TESEA's Commitment to Quality and Innovation
No full textDeputy Editor Oscar Acevedo discusses the journal's most recent achievements and commitment to quality and innovation. The editorial highlights TESEA's inclusion in SCOPUS in 2023, its Q4 ranking in the Scimago Journal Rank (SJR), and its acceptance for indexing in the Directory for Open Access Journals (DOAJ)
Exploring out-of-focus camera calibration for improved UAV survey accuracy
No full textCalibrating large-range vision systems like UAV cameras is a complex task that often involves costly setups and the potential for errors due to inaccuracies in target fabrication. Traditional UAV surveying software typically estimates camera parameters alongside ground control points, but this method may lack optimal accuracy. Our study explores an alternative: using out-of-focus camera calibration to improve the reliability and accuracy of drone cameras for surveying. In our approach, the UAV camera is positioned several meters away from a low-cost target to ensure focus. We then calibrate the intrinsic camera parameters using an out-of-focus small calibration target, fixing these parameters before flight. For evaluation, we compare this method against the standard approach of estimating UAV camera parameters with survey imagery. Preliminary results suggest that this out-of-focus method offers a reliable and accurate solution for UAV surveying applications
Social Protest as a Fundamental Right from the Colombian Constitutional Court
No full textLa Constitución Política colombiana de 1991 se adscribe a una iusteoría antifor-malista, por la cual el derecho tiene una naturaleza abierta que debe ser complemen-tada por las interpretaciones auténticas de los jueces constitucionales. Tal es el caso del derecho a la protesta social en Colombia, el cual es una construcción judicial auténtica que establece los lineamientos para que las autoridades garanticen este derecho desde el alcance dinámico de la manifestación social y se eviten abusos de poder por parte del Estado. Así, se trata de un nuevo derecho fundamental que rompe paradigmas y tiene la vocación de materializar los principios democráticos desde la realidad social.The Colombian Political Constitution of 1991 adheres to an anti-formalist iustheory, by which the law has an open nature that must be complemented by the authentic
interpretations of constitutional judges. Such is the case of the right to social protest
in Colombia, which is an authentic judicial construction that establishes the guidelines for the authorities to guarantee this right from the dynamic scope of the social
demonstration and avoid abuses of power by the State. Thus, it is a new fundamental
right that breaks paradigms and has the vocation of materializing democratic principles from the social reality.Universidad Tecnológica de BolívarSUMARIO
I. Introducción y diseño metodológico
II. La doctrina constitucional como fuente legítima para interpretar los derechos fundamentales en Colombia.
III. Construcción del derecho fundamental colombiano a la protesta social desde la doctrina constitucional.
1. Sentencias fundadoras de la línea jurisprudencial sobre la protesta social.
2. Estado actual de la protesta social desde la doctrina constitucional.
IV. Conclusiones.
V. Bibliografía
Economic scheduling and dispatching of distributed generators considering uncertainties in modified 33-bus and modified 69-bus system under different microgrid regions
No full textThis paper presents a comprehensive framework for the economic scheduling and dispatching of Distributed Generators (DGs) in modified 33-bus and 69-bus systems across multi-microgrid regions. The framework introduces two key techniques: a novel dispatch strategy for optimizing the charging and discharging of Electric Vehicle (EV) batteries, and a robust power dispatch method for islanded distribution systems. The EV dispatch strategy uses a multi-criteria decision analysis method, Probabilistic Elimination and Choice Expressing Reality (p-ELECTRE), to maximize profits for EV owners while meeting power system requirements. This strategy is tested on fleets of 100 and 200 EVs with random travel plans within the modified 33-bus and 69-bus systems, and employs the BAT Optimization Algorithm (BOA) for optimal power dispatch. The second technique addresses the power dispatch in islanded systems by sectionalizing them into self-supplied microgrids, aiming to minimize operational costs, system losses, and voltage deviation using the Jaya algorithm. Additionally, a multi-objective cost-effective emission dispatch is evaluated using Whale Optimization Algorithm (WOA), showing superior performance over Differential Evolution (DE), Particle Swarm Optimization (PSO), and Grey Wolf Optimization (GWO). Comparative analysis highlights the scalability and adaptability of the proposed approach, making it a valuable tool for efficient microgrid management. Simulation results confirm significant improvements in cost savings, system reliability, and operational efficiency under various uncertainty scenarios