Diseño y desarrollo de un producto/servicio para la mejora de la experiencia de usuario en el entorno del esquí

Este proyecto se ha realizado durante mi estancia de Erasmus en Wels, Austria, en el curso 2019/2020. El proyecto surge de la combinación del proceso de diseño User Experience y la posibilidad de realizar el trabajo de campo en los Alpes austriacos. Partiendo de estos dos aspectos se propone el diseño y desarrollo de un producto/servicio que mejore la experiencia de usuario en el entorno del esquí, finalmente centrándonos en el ámbito de la salud durante esta actividad. User Experience crea productos que generan experiencias significativas y relevantes para los usuarios. Posicionar al usuario en el centro del proyecto es uno de los objetivos principales. Esto quiere decir que el proyecto tiene en cuenta el proceso completo, incluyendo diseño, usabilidad y funcionalidad.El producto final es un servicio para la jornada de esquí que consiste en el seguimiento de unos parámetros determinados de salud de los usuarios y en educar a estos en los diferentes aspectos de salud con el fin de fomentar acciones responsables durante el esquí. <br /

Desarrollo de recursos gráficos para la docencia de design sketching

El trabajo consiste en el diseño de recursos gráficos (ejercicios y rúbricas) para mejorar la docencia en design sketching, más concretamente del bloque de exploración formal. Además, debido a la actual situación de pandemia de COVID-19, las metodologías de enseñanza se han tenido que adaptar a la educación online a distancia. Esto ha supuesto un problema debido a la falta de herramientas para enseñar disciplinas como el sketching tradicional. Por ello, se ha indagado en cómo poder aportar una experiencia completa en cuanto a interactividad, participación, adquisición de conocimientos y feedback para mejorar la enseñanza de design sketching online.<br /

Urinary Vitamin D Binding Protein and KIM-1 Are Potent New Biomarkers of Major Adverse Renal Events in Patients Undergoing Coronary Angiography

Background Vitamin-D-binding protein (VDBP) is a low molecular weight protein that is filtered through the glomerulus as a 25-(OH) vitamin D 3/VDBP complex. In the normal kidney VDBP is reabsorbed and catabolized by proximal tubule epithelial cells reducing the urinary excretion to trace amounts. Acute tubular injury is expected to result in urinary VDBP loss. The purpose of our study was to explore the potential role of urinary VDBP as a biomarker of an acute renal damage. Method We included 314 patients with diabetes mellitus or mild renal impairment undergoing coronary angiography and collected blood and urine before and 24 hours after the CM application. Patients were followed for 90 days for the composite endpoint major adverse renal events (MARE: need for dialysis, doubling of serum creatinine after 90 days, unplanned emergency rehospitalization or death). Results Increased urine VDBP concentration 24 hours after contrast media exposure was predictive for dialysis need (no dialysis: 113.06 ± 299.61ng/ml, n = 303; need for dialysis: 613.07 ± 700.45 ng/ml, n = 11, Mean ± SD, p<0.001), death (no death during follow-up: 121.41 ± 324.45 ng/ml, n = 306; death during follow-up: 522.01 ± 521.86 ng/ml, n = 8; Mean ± SD, p<0.003) and MARE (no MARE: 112.08 ± 302.00ng/ml, n = 298; MARE: 506.16 ± 624.61 ng/ml, n = 16, Mean ± SD, p<0.001) during the follow-up of 90 days after contrast media exposure. Correction of urine VDBP concentrations for creatinine excretion confirmed its predictive value and was consistent with increased levels of urinary Kidney Injury Molecule-1 (KIM-1) and baseline plasma creatinine in patients with above mentioned complications. The impact of urinary VDBP and KIM-1 on MARE was independent of known CIN risk factors such as anemia, preexisting renal failure, preexisting heart failure, and diabetes. Conclusions Urinary VDBP is a promising novel biomarker of major contrast induced nephropathy-associated events 90 days after contrast media exposure

Artificial intelligence for climate prediction of extremes: state of the art, challenges, and future perspectives

Extreme events such as heat waves and cold spells, droughts, heavy rain, and storms are particularly challenging to predict accurately due to their rarity and chaotic nature, and because of model limitations. However, recent studies have shown that there might be systemic predictability that is not being leveraged, whose exploitation could meet the need for reliable predictions of aggregated extreme weather measures on timescales from weeks to decades ahead. Recently, numerous studies have been devoted to the use of artificial intelligence (AI) to study predictability and make climate predictions. AI techniques have shown great potential to improve the prediction of extreme events and uncover their links to large‐scale and local drivers. Machine and deep learning have been explored to enhance prediction, while causal discovery and explainable AI have been tested to improve our understanding of the processes underlying predictability. Hybrid predictions combining AI, which can reveal unknown spatiotemporal connections from data, with climate models that provide the theoretical foundation and interpretability of the physical world, have shown that improving prediction skills of extremes on climate‐relevant timescales is possible. However, numerous challenges persist in various aspects, including data curation, model uncertainty, generalizability, reproducibility of methods, and workflows. This review aims at overviewing achievements and challenges in the use of AI techniques to improve the prediction of extremes at the subseasonal to decadal timescale. A few best practices are identified to increase trust in these novel techniques, and future perspectives are envisaged for further scientific development

Inferring causation from time series in Earth system sciences

The heart of the scientific enterprise is a rational effort to understand the causes behind the phenomena we observe. In large-scale complex dynamical systems such as the Earth system, real experiments are rarely feasible. However, a rapidly increasing amount of observational and simulated data opens up the use of novel data-driven causal methods beyond the commonly adopted correlation techniques. Here, we give an overview of causal inference frameworks and identify promising generic application cases common in Earth system sciences and beyond. We discuss challenges and initiate the benchmark platform causeme.net to close the gap between method users and developers. © 2019, The Author(s)

Non-orographic gravity waves and turbulence caused by merging jet streams

Jet streams are important sources of non-orographic internal gravity waves and clear air turbulence (CAT). We analyze non-orographic gravity waves and CAT during a merger of the polar front jet stream (PFJ) with the subtropical jet stream (STJ) above the southern Atlantic. Thereby, we use a novel combination of airborne observations covering the meso-scale and turbulent scale in combination with high-resolution deterministic short-term forecasts. Coherent phase lines of temperature perturbations by gravity waves stretching along a highly sheared tropopause fold are simulated by the ECMWF IFS (integrated forecast system) forecasts. During the merging event, the PFJ reverses its direction from approximately antiparallel to parallel with respect to the STJ, going along with strong wind shear and horizontal deformation. Temperature perturbations in limb-imaging and lidar observations onboard the research aircraft HALO during the SouthTRAC campaign show remarkable agreement with the IFS data. Ten hours earlier, the IFS data show an “X-shaped” pattern in the temperature perturbations emanating from the sheared tropopause fold. Tendencies of the IFS wind components show that these gravity waves are excited by spontaneous emission adjusting the strongly divergent flow when the PFJ impinges the STJ. In situ observations of temperature and wind components at 100 Hz confirm upward propagation of the probed portion of the gravity waves. They furthermore reveal embedded episodes of light-to-moderate CAT, Kelvin Helmholtz waves, and indications for partial wave reflection. Patches of low Richardson numbers in the IFS data coincide with the CAT observations, suggesting that this event was accessible to turbulence forecasting

Slowing of acoustic waves in electrorheological and string-fluid complex plasmas

The PK-4 laboratory consists of a direct current plasma tube into which microparticles are injected, forming a complex plasma. The microparticles acquire many electrons from the ambient plasma and are thus highly charged and interact with each other. If ion streams are present, wakes form downstream of the microparticles, which lead to an attractive term in the potential between the microparticles, triggering the appearance of microparticle strings and modifying the complex plasma into an electrorheological form. Here we report on a set of experiments on compressional waves in such a string fluid in the PK-4 laboratory during a parabolic flight and on board the International Space Station. We find a slowing of acoustic waves and hypothesize that the additional attractive interaction term leads to slower wave speeds than in complex plasmas with purely repulsive potentials. We test this hypothesis with simulations, and compare with theory

Terrestrial laser scanning in forest inventories

AbstractDecision making on forest resources relies on the precise information that is collected using inventory. There are many different kinds of forest inventory techniques that can be applied depending on the goal, scale, resources and the required accuracy. Most of the forest inventories are based on field sample. Therefore, the accuracy of the forest inventories depends on the quality and quantity of the field sample. Conventionally, field sample has been measured using simple tools. When map is required, remote sensing materials are needed. Terrestrial laser scanning (TLS) provides a measurement technique that can acquire millimeter-level of detail from the surrounding area, which allows rapid, automatic and periodical estimates of many important forest inventory attributes. It is expected that TLS will be operationally used in forest inventories as soon as the appropriate software becomes available, best practices become known and general knowledge of these findings becomes more wide spread. Meanwhile, mobile laser scanning, personal laser scanning, and image-based point clouds became capable of capturing similar terrestrial point cloud data as TLS. This paper reviews the advances of applying TLS in forest inventories, discusses its properties with reference to other related techniques and discusses the future prospects of this technique