Publicaciones
Repositorio Institucional
Navegar
Slide 1 of 5 Publicación Acceso Abierto
Patterns of media and social media consumption associated with suicidal ideation in Spanish children
(Humanities and social sciences communications, 2026-01-23) Barredo-Ibáñez, Daniel; Garcés Prettel, Miguel Efrén; Caro-Castaño, Lucía; Vega-Saldaña, Silvia; Merchán-Clavellino, Ana; Santoya Montes, Yanin Elena; Arroyave-Cabrera, Jesús; Grupo de Investigación en Estudios Sociales y Humanísticos- GESH; Semillero de Investigación en Periodismo
Child suicide is a critical public health issue in Spain. From 2020 to 2021, the child suicide rate rose by approximately 57%, with 22 children aged 15 or younger taking their own lives in 2021 compared to 14 in 2020. This phenomenon is complex, with psychological, social, and economic factors potentially influencing a young person’s decision to end their life. Among these, some researchers caution that certain media and social media exposures may increase or mitigate suicidal ideation, which remains the key factor to address in preventing child suicide. This study primarily aims to compare differences in suicidal ideation among Spanish children exposed to various types of media and social media content. Specifically, we focus on a) identifying which media formats might be protective or increase risk in this population; and b) identifying factors associated with suicidal ideation related to media and social media use in this age group. In this non-experimental study, we surveyed 804 Spanish children aged 10 to 15 years. Our multivariable models identified three variables consistently associated with higher odds of suicidal ideation: frequent online searches for information about suicide, regularly posting private photos on social media, and regularly viewing contests or reality shows.
Slide 2 of 5 Publicación Acceso Abierto
Energy performance of gas turbine compressor stations operating with hydrogen–natural gas blends
(ASME, 2025-10-30) Fajardo Cuadro, Juan Gabriel; Barreto Ponton, Deibys; Yabrudy Daniel; Rangel Richard; Garcia Samira; Sanjuan Marco
Hydrogen is a rising energy carrier for decarbonization. One possible use is blending it with natural gas, but researchers must address performance issues in thermal systems. In this study, we evaluate the performance of a natural gas compression system with a gas turbine using energetic, exergetic, and exergoeconomic analyses for different hydrogen–natural gas blends. The analyses reveal a reduction of 187.5 kg CO2 per ton of fuel for every 10% increase in hydrogen content. Furthermore, air and fuel requirements decrease by 15%, while compression train energy efficiency improves by 27.21% in certain blends. However, the specific cost of gas rises to $20.56/GJ when using pure hydrogen. Finally, increasing the hydrogen fraction reduces CO2 emissions but also raises costs.
Slide 3 of 5 Publicación Acceso Abierto
Vision-based CNN prediction of sunspot numbers from SDO/HMI images
(Solar and Stellar Astrophysics, 2026-03-18) Quintero Pareja, Fabian Camilo; Montaño Burbano, Diederik Antonio; Quintero Pareja, Santiago; Sierra Porta, David; Grupo de Investigación Gravitación y Matemática Aplicada; Semillero de Investigación en Astronomía y Ciencia de Datos
Sunspot numbers provide the longest continuous record of solar activity and remain a key index for heliophysical research and space-weather applications. Standard sunspot determination relies on visual inspection and algorithmic feature-detection pipelines, both of which involve methodological choices and can be sensitive to image quality and implementation details. Convolutional neural networks (CNNs) offer an alternative by learning an end-to-end mapping from solar images to a scalar index, reducing reliance on explicit, handcrafted feature design. Here we present a supervised vision-based regression framework to estimate the daily sunspot number from full-disk continuum images acquired by the Helioseismic and Magnetic Imager (HMI) onboard NASA Solar Dynamics Observatory (SDO). We pair daily images from 2011-2024 with the SILSO Version 2.0 daily sunspot number and train a CNN to infer the scalar value at the observation time of each image. On an independent test split, the model achieves R2=0.964, RMSE=9.75, and MAE=6.74, indicating close agreement with SILSO across a wide activity range. Interpretability analyses using Grad-CAM and Integrated Gradients show that the network attributions concentrate on sunspot-bearing regions, supporting the physical plausibility of the learned representations. These results demonstrate the feasibility of direct image-to-index estimation for scalable solar monitoring. Future work will explore multimodal fusion with complementary observables (e.g., magnetograms) and standardized cross-cycle benchmarks to assess robustness under changing solar conditions.
Slide 4 of 5
Slide 5 of 5
