Using the Dipolar and Quadrupolar Moments to Improve Solar-Cycle Predictions Based on the Polar Magnetic Fields

The solar cycle and its associated magnetic activity are the main drivers behind changes in the interplanetary environment and Earth's upper atmosphere (commonly referred to as space weather and climate). In recent years there has been an effort to develop accurate solar cycle predictions, leading to nearly a hundred widely spread predictions for the amplitude of solar cycle 24. Here we show that cycle predictions can be made more accurate if performed separately for each hemisphere, taking advantage of information about both the dipolar and quadrupolar moments of the solar magnetic field during minimum

Efficient labeling of solar flux evolution videos by a deep learning model

Machine learning (ML) is becoming a critical tool for interrogation of large complex data. Labeling, defined as the process of adding meaningful annotations, is a crucial step of supervised ML. However, labeling datasets is time consuming. Here we show that convolutional neural networks (CNNs), trained on crudely labeled astronomical videos, can be leveraged to improve the quality of data labeling and reduce the need for human intervention. We use videos of the solar magnetic field, crudely labeled into two classes: emergence or non-emergence of bipolar magnetic regions (BMRs), based on their first detection on the solar disk. We train CNNs using crude labels, manually verify, correct labeling vs. CNN disagreements, and repeat this process until convergence. Traditionally, flux emergence labelling is done manually. We find that a high-quality labeled dataset, derived through this iterative process, reduces the necessary manual verification by 50%. Furthermore, by gradually masking the videos and looking for maximum change in CNN inference, we locate BMR emergence time without retraining the CNN. This demonstrates the versatility of CNNs for simplifying the challenging task of labeling complex dynamic events.Comment: 16 pages, 7 figures, published in Nature Astronomy, June 27, 202

La construcción de opinión pública a partir de la videoblogger La Pulla 2019

Analizar el contenido de opinión pública a partir del videoblog de la influencer La Pulla 2019.La investigación tiene como propósito analizar de qué manera los influencer hacen parte de la construcción de opinión pública, lo que se quiere es identificar cómo las redes sociales han ejercido una configuración o influencia de contenidos públicos en donde este material juega un papel importante al momento de crear una opinión. Estará enfocado básicamente en analizar el contenido visual y los comentarios que se hacen de estas publicaciones, para así observar el por qué está el interés en los usuarios en las publicaciones del caso de estudio en particular sobre la influencer La Pulla. Con esta investigación el objetivo es saber de qué manera responden las personas que son miembros de esta red social a los diferentes contenidos de opinión pública que están recibiendo en sus perfiles. Analizaremos dos tendencias que son fundamentales en el esquema de los influencer en el sitio web YouTube: los contenidos que suben los usuarios y los comentarios que tienen frente a las publicaciones del canal de la Pulla. Se investigó ¿por qué las personas usan la red social YouTube para ver este tipo de Influencer en este caso La Pulla?, ¿Cuáles son los temas de interés que desean explorar?, ¿Cuáles son los pensamientos e ideas que quiere transmitir esta Influencer en sus publicaciones? Y ¿por qué La Pulla hace este tipo de contenidos para sus seguidores? En el proyecto se verán dos fases que son: en primera instancia la planeación del problema, los objetivos y la metodología que se va a llevar a cabo en el desarrollo de la investigación, y un segundo momento que es en el que se mostrará los resultados y hallazgos que se obtuvieron en medio del proceso investigación y la formulación del trabajo

Homogenising SoHO/EIT and SDO/AIA 171\AA ~ Images: A Deep Learning Approach

Extreme Ultraviolet images of the Sun are becoming an integral part of space weather prediction tasks. However, having different surveys requires the development of instrument-specific prediction algorithms. As an alternative, it is possible to combine multiple surveys to create a homogeneous dataset. In this study, we utilize the temporal overlap of SoHO/EIT and SDO/AIA 171~\AA ~surveys to train an ensemble of deep learning models for creating a single homogeneous survey of EUV images for 2 solar cycles. Prior applications of deep learning have focused on validating the homogeneity of the output while overlooking the systematic estimation of uncertainty. We use an approach called `Approximate Bayesian Ensembling' to generate an ensemble of models whose uncertainty mimics that of a fully Bayesian neural network at a fraction of the cost. We find that ensemble uncertainty goes down as the training set size increases. Additionally, we show that the model ensemble adds immense value to the prediction by showing higher uncertainty in test data that are not well represented in the training data.Comment: 20 pages, 8 figures, accepted for publication in ApJ

MEMPSEP I : Forecasting the Probability of Solar Energetic Particle Event Occurrence using a Multivariate Ensemble of Convolutional Neural Networks

The Sun continuously affects the interplanetary environment through a host of interconnected and dynamic physical processes. Solar flares, Coronal Mass Ejections (CMEs), and Solar Energetic Particles (SEPs) are among the key drivers of space weather in the near-Earth environment and beyond. While some CMEs and flares are associated with intense SEPs, some show little to no SEP association. To date, robust long-term (hours-days) forecasting of SEP occurrence and associated properties (e.g., onset, peak intensities) does not effectively exist and the search for such development continues. Through an Operations-2-Research support, we developed a self-contained model that utilizes a comprehensive dataset and provides a probabilistic forecast for SEP event occurrence and its properties. The model is named Multivariate Ensemble of Models for Probabilistic Forecast of Solar Energetic Particles (MEMPSEP). MEMPSEP workhorse is an ensemble of Convolutional Neural Networks that ingests a comprehensive dataset (MEMPSEP III - (Moreland et al., 2023)) of full-disc magnetogram-sequences and in-situ data from different sources to forecast the occurrence (MEMPSEP I - this work) and properties (MEMPSEP II - Dayeh et al. (2023)) of a SEP event. This work focuses on estimating true SEP occurrence probabilities achieving a 2.5% improvement in reliability and a Brier score of 0.14. The outcome provides flexibility for the end-users to determine their own acceptable level of risk, rather than imposing a detection threshold that optimizes an arbitrary binary classification metric. Furthermore, the model-ensemble, trained to utilize the large class-imbalance between events and non-events, provides a clear measure of uncertainty in our forecastComment: 17 pages, 8 figures, 1 table, accepted for publication in Space Weather journa

The Minimum of Solar Cycle 23: As Deep as It Could Be?

In this work we introduce a new way of binning sunspot group data with the purpose of better understanding the impact of the solar cycle on sunspot properties and how this defined the characteristics of the extended minimum of cycle 23. Our approach assumes that the statistical properties of sunspots are completely determined by the strength of the underlying large-scale field and have no additional time dependencies. We use the amplitude of the cycle at any given moment (something we refer to as activity level) as a proxy for the strength of this deep-seated magnetic field. We find that the sunspot size distribution is composed of two populations: one population of groups and active regions and a second population of pores and ephemeral regions. When fits are performed at periods of different activity level, only the statistical properties of the former population, the active regions, is found to vary. Finally, we study the relative contribution of each component (small-scale versus large-scale) to solar magnetism. We find that when hemispheres are treated separately, almost every one of the past 12 solar minima reaches a point where the main contribution to magnetism comes from the small-scale component. However, due to asymmetries in cycle phase, this state is very rarely reached by both hemispheres at the same time. From this we infer that even though each hemisphere did reach the magnetic baseline, from a heliospheric point of view the minimum of cycle 23 was not as deep as it could have been

Algorithm for the prediction of the reactive forces developed in the socket of transfemoral amputees

Based on a mathematical model of the human gait, a Matlab 2010a algorithm is presented to predict the reaction forces and moments in a particular point along the socket linked to the lower limb of a transfemoral amputee. The model takes the inertia developed due the swing of the limb during the gait into consideration. A validation of the results is made with the data obtained in a gait lab, and the model results are consistent with those obtained in the gait lab

Diseño de moda con integración digital

Con la implementación de tecnología en diversas industrias y la rápida apropiación por parte de los usuarios, más aún debido al Covid-19, el mundo de la producción y del consumo se está transformado. La industria de la moda no puede ser ajena a este fenómeno, hoy la demanda exige nuevos modelos de negocio y propuestas capaces de sorprender a los usuarios. El objetivo del proyecto es diseñar una metodología de cocreación con herramientas de prototipado digital para el desarrollo de una colección virtual

Epidemiologia da síndrome coronária aguda

Cardiovascular diseases are the main cause of mortality all over the world. Their incidence and prevalence increase with age and different risk factors. The majority of factors are closely related to the lifestyle; aspects such as hypertension, diabetes mellitus, obesity, and stress are more prevalent day by day and vary according to the population and its geographical location. The researchers made a revision on international and national epidemiology of the acute coronary syndrome and it has changed over the years. The study included literature published in both English and Spanish retrieved from different databases. For this particular case, 50 articles focused on the epidemiologic impact of the acute coronary syndrome were reviewed. After a detailed revision, it can be said that the cardiovascular disease, which has been increasing during the past years, remains as the first cause of morbidity and mortality at the worldwide level. This syndrome causes more deaths in all events, with different incidence, age in which it takes place, economic impact and risk factors, depending on each region. The modifiable risk factors are still very important in the development of cardiovascular diseasesLas enfermedades cardiovasculares son la principal causa de muerte en el mundo. Su incidencia y prevalencia aumentan con la edad y con los diferentes factores de riesgo. La mayoría de estos factores tienen una relación estrecha con el estilo de vida; factores como la dislipidemia, el tabaquismo, la hipertensión, la diabetes mellitus, la obesidad y el estrés son cada vez más prevalentes y varían según la población y localización geográfica. Por eso se realiza una revisión de la epidemiología mundial y nacional del síndrome coronario agudo, y de cómo este ha variado a través de los años. Se buscó literatura en inglés y español en diferentes bases de datos, fueron seleccionados 50 artículos que se presentan haciendo énfasis en el impacto epidemiológico del síndrome coronario agudo. Luego de realizar la revisión detallada se concluye que la enfermedad cardiovascular persiste como primera causa mundial de morbimortalidad, con aumento en su frecuencia durante los últimos años. El síndrome coronario agudo es la afección que provoca más muertes entre todos los eventos, con incidencia, edad de presentación, impacto económico y factores de riesgo diferentes de acuerdo con cada región. Los factores de riesgo modificables siguen siendo muy importantes en el desarrollo de enfermedades cardiovasculares.As doenças cardiovasculares são as principais causas de morte no mundo. Sua incidência e prevalência aumentam com a idade e com os diferentes fatores de risco. A maioria destes fatores têm uma relação estreita com o estilo de vida; fatores como a dislipidemia, o tabaquismo, a hipertensão, a diabetes mellitus, a obesidade e o estresse são cada vez mais prevalentes e variam segundo a população e localização geográfica. Por isso se realiza uma revisão da epidemiologia mundial e nacional da síndrome coronária aguda, e de como este há variado através dos anos. Se buscou literatura em inglês e espanhol em diferentes bases de dados, foram selecionados 50 artigos que se apresentam fazendo ênfase no impacto epidemiológico da síndrome coronária aguda. Logo de realizar a revisão detalhada se conclui que a doença cardiovascular persiste como primeira causa mundial de morbimortalidade, com aumento na sua frequência durante os últimos anos. A síndrome coronária aguda é a afecção que provoca mais mortes entre todos os eventos, com incidência, idade de apresentação, impacto econômico e fatores de risco diferentes de acordo com cada região. Os fatores de risco modificáveis seguem sendo muito importantes no desenvolvimento de doenças cardiovasculares