Patrones sinópticos aplicados a la predicción en la península antártica

Ponencia presentada en: VI Simposio Nacional de Predicción, celebrado en los servicios centrales de AEMET, en Madrid, del 17 al 19 de septiembre de 2018.Aplicando el método de análisis mediante clústeres al campo de presión en superficie de los reanálisis del modelo ERA Interim, se han definido cinco patrones para el área del paso de Drake y la península antártica. El análisis de frecuencias muestra que los cinco presentan una ocurrencia anual similar pero una gran variabilidad estacional, y la persistencia de cada uno es relativamente alta. La transición entre patrones diferentes tiende a seguir un ciclo acorde con el desplazamiento de un cuarto de onda hacia el este de las ondas sinópticas. El estudio de las configuraciones típicas de la región, basado en los patrones obtenidos, y su relación con el tiempo asociado en superficie (en particular, efemérides) en las bases antárticas españolas (BAE) favorecerá la comprensión de la meteorología de la zona. Además, la aplicación de estos patrones a situaciones cotidianas facilitará y hará más eficiente el trabajo diario de predicción operativa en las campañas antárticas españolas

Actividades de AEMET en la Antártida

Póster presentado en: VIII Simposio de Estudios Polares, celebrado en Palma de Mallorca, del 7 al 9 de septiembre de 201

Instalaciones de la AEMET en la Antártida

Póster presentado en: VIII Simposio de Estudios Polares, celebrado en Palma de Mallorca, del 7 al 9 de septiembre de 201

Despertar la curiosidad por la química desde la Universidad

En este trabajo se presentará la experiencia realizada a partir de la visita a la exposición “¿Dónde está la química?”, por parte de estudiantes de secundaria, ciclos formativos y bachillerato. Esta exposición se ha programado desde la Escola Universitària Politècnica de Manresa de la Universitat Politècnica de Catalunya, con el objetivo de acercar a los estudiantes de este nivel al conocimiento y, más que nada, a la curiosidad por el mundo de la química. Se expondrá en primer lugar el material que se presenta en la exposición, así como los resultados obtenidos en una encuesta realizada a los asistentes una vez concluida la visita, y en segundo lugar se presentarán las conclusiones obtenidas de la valoración indicada.Peer ReviewedPostprint (published version

Effectiveness of an intervention for improving drug prescription in primary care patients with multimorbidity and polypharmacy:Study protocol of a cluster randomized clinical trial (Multi-PAP project)

This study was funded by the Fondo de Investigaciones Sanitarias ISCIII (Grant Numbers PI15/00276, PI15/00572, PI15/00996), REDISSEC (Project Numbers RD12/0001/0012, RD16/0001/0005), and the European Regional Development Fund ("A way to build Europe").Background: Multimorbidity is associated with negative effects both on people's health and on healthcare systems. A key problem linked to multimorbidity is polypharmacy, which in turn is associated with increased risk of partly preventable adverse effects, including mortality. The Ariadne principles describe a model of care based on a thorough assessment of diseases, treatments (and potential interactions), clinical status, context and preferences of patients with multimorbidity, with the aim of prioritizing and sharing realistic treatment goals that guide an individualized management. The aim of this study is to evaluate the effectiveness of a complex intervention that implements the Ariadne principles in a population of young-old patients with multimorbidity and polypharmacy. The intervention seeks to improve the appropriateness of prescribing in primary care (PC), as measured by the medication appropriateness index (MAI) score at 6 and 12months, as compared with usual care. Methods/Design: Design:pragmatic cluster randomized clinical trial. Unit of randomization: family physician (FP). Unit of analysis: patient. Scope: PC health centres in three autonomous communities: Aragon, Madrid, and Andalusia (Spain). Population: patients aged 65-74years with multimorbidity (≥3 chronic diseases) and polypharmacy (≥5 drugs prescribed in ≥3months). Sample size: n=400 (200 per study arm). Intervention: complex intervention based on the implementation of the Ariadne principles with two components: (1) FP training and (2) FP-patient interview. Outcomes: MAI score, health services use, quality of life (Euroqol 5D-5L), pharmacotherapy and adherence to treatment (Morisky-Green, Haynes-Sackett), and clinical and socio-demographic variables. Statistical analysis: primary outcome is the difference in MAI score between T0 and T1 and corresponding 95% confidence interval. Adjustment for confounding factors will be performed by multilevel analysis. All analyses will be carried out in accordance with the intention-to-treat principle. Discussion: It is essential to provide evidence concerning interventions on PC patients with polypharmacy and multimorbidity, conducted in the context of routine clinical practice, and involving young-old patients with significant potential for preventing negative health outcomes. Trial registration: Clinicaltrials.gov, NCT02866799Publisher PDFPeer reviewe

Advanced Driver Assistance Systems (ADAS) Based on Machine Learning Techniques for the Detection and Transcription of Variable Message Signs on Roads

Among the reasons for traffic accidents, distractions are the most common. Although there are many traffic signs on the road that contribute to safety, variable message signs (VMSs) require special attention, which is transformed into distraction. ADAS (advanced driver assistance sys-tem) devices are advanced systems that perceive the environment and provide assistance to the driver for his comfort or safety. This project aims to develop a prototype of a VMS (variable mes-sage sign) reading system using machine learning techniques, which are still not used, especially in this aspect. The assistant consists of two parts: a first one that recognizes the signal on the street and another one that extracts its text and transforms it into speech. For the first one, a set of im-ages were labeled in PASCAL VOC format by manual annotations, scraping and data augmenta-tion. With this dataset, the VMS recognition model was trained, a RetinaNet based off of Res-Net50 pretrained on the dataset COCO. Firstly, in the reading process, the images were prepro-cessed and binarized to achieve the best possible quality. Finally, the extraction was done by the Tesseract OCR model in its 4.0 version, and the speech was done by the cloud service of IBM Watson Text to Speech.Plan Nacional para la Investigación PN I+D+i (PID2019-104793RB-C32)Comunidad de Madrid. SEGVAUTO-4.0-CM (P2018/EMT-4362)3.576 JCR (2020) Q1, 14/64 Instruments & Instrumentation0.636 SJR (2020) Q2, 46/121 Analytical ChemistryNo data IDR 2019UE

Dataset: Roundabout Aerial Images for Vehicle Detection

This publication presents a dataset of Spanish roundabouts aerial images taken from a UAV, along with annotations in PASCAL VOC XML files that indicate the position of vehicles within them. Additionally, a CSV file is attached containing information related to the location and characteristics of the captured roundabouts. This work details the process followed to obtain them: image capture, processing, and labeling. The dataset consists of 985,260 total instances: 947,400 cars, 19,596 cycles, 2262 trucks, 7008 buses, and 2208 empty roundabouts in 61,896 1920 × 1080 px JPG images. These are divided into 15,474 extracted images from 8 roundabouts with different traffic flows and 46,422 images created using data augmentation techniques. The purpose of this dataset is to help research into computer vision on the road, as such labeled images are not abundant. It can be used to train supervised learning models, such as convolutional neural networks, which are very popular in object detection.Proyecto I+D+i (PID2019-104793RB-C32)Proyecto I+D+i (PIDC2021- 121517-C33)MCIN/AEI/10.13039/501100011033/Comunidad de Madrid (S2018/EMT-4362/“SEGVAUTO4.0-CM”)No data JCR 20200.560 SJR (2021) Q2, 322/729 Computer Science ApplicationsNo data IDR 2020UE

Dataset: Variable Message Signal Annotated Images for Object Detection

This publication presents a dataset consisting of Spanish road images taken from inside a vehicle, as well as annotations in XML files in PASCAL VOC format that indicate the location of Variable Message Signals within them. Additionally, a CSV file is attached with information regarding the geographic position, the folder where the image is located and the text in Spanish. This can be used to train supervised learning computer vision algorithms such as convolutional neural networks. Throughout this work, the process followed to obtain the dataset, image acquisition and labeling and its specifications are detailed. The dataset constitutes 1216 instances, 888 positives and 328 negatives, in 1152 jpg images with a resolution of 1280 × 720 pixels. These are divided into 756 real images and 756 images created from the data-augmentation technique. The purpose of this dataset is to help in road computer vision research since there is not one specifically for VMSs.Proyecto I+D+i (PID2019-104793RB-C32)Proyecto I+D+i (PIDC2021- 121517-C33)MCIN/AEI/10.13039/501100011033/Comunidad de Madrid (S2018/EMT-4362/“SEGVAUTO4.0-CM”)No data JCR 20200.560 SJR (2021) Q2, 322/729 Computer Science ApplicationsNo data IDR 2020UE