Deep Learning Models for Passability Detection of Flooded Roads

In this paper we study and compare several approaches to detect floods and evidence for passability of roads by conventional means in Twitter. We focus on tweets containing both visual information (a picture shared by the user) and metadata, a combination of text and related extra information intrinsic to the Twitter API. This work has been done in the context of the MediaEval 2018 Multimedia Satellite Task

Características de personalidad percibidas en los padres y la pareja permanente: un estudio correlacional

Diversos autores afirman que la elección de pareja se basa principalmente en encontrar características semejantes a las de los padres. Así, las mujeres buscarían un hombre que se parezca a su padre, mientras que los hombres tenderían a elegir una mujer parecida a su madre. Por ello, se realizó una investigación con el fin de encontrar si había correlación entre las características percibidas en las características de autoconcepto de los padres y el de la pareja permanente, para lo cual se utilizó un cuestionario elaborado ex profeso. Se halló que los hombres perciben una mayor relación entre su madre y su pareja, en comparación con las mujeres, concluyéndose que en ambos sexos existe una clara tendencia a vincularse con una pareja sobre la base del tipo de relación que se tuvo con los padres

Deep learning models for road passability detection during flood events using social media data

During natural disasters, situational awareness is needed to understand the situation and respond accordingly. A key need is assessing open roads for transporting emergency support to victims. This can be done via analysis of photos from affected areas with known location. This paper studies the problem of detecting blocked / open roads from photos during floods by applying a two-step approach based on classifiers: does the image have evidence of road? If it does, is the road passable or not? We propose a single double-ended neural network (NN) architecture which addresses both tasks at the same time. Both problems are treated as a single class classification problem by the usage of a compactness loss. The study is performed on a set of tweets, posted during flooding events, that contain (i)~metadata and (ii)~visual information. We study the usefulness of each source of data and the combination of both. Finally, we do a study of the performance gain from ensembling different networks. Through the experimental results we prove that the proposed double-ended NN makes the model almost two times faster and memory lighter while improving the results with respect to training two separate networks to solve each problem independently

AI-Based Flood Event Understanding and Quantifying Using Online Media and Satellite Data

In this paper we study the problem of flood detection and quantification using online media and satellite data. We present a three approaches, two of them based on neural networks and a third one based on the combination of different bands of satellite images. This work aims to detect floods and also give relevant information about the flood situation such as the water level and the extension of the flooded regions, as specified in the three subtasks, for which of them we propose a specific solution

Multi-modal Deep Learning Approach for Flood Detection

In this paper we propose a multi-modal deep learning approach to detect floods in social media posts. Social media posts normally contain some metadata and/or visual information, therefore in order to detect the floods we use this information. The model is based on a Convolutional Neural Network which extracts the visual features and a bidirectional Long Short-Term Memory network to extract the semantic features from the textual metadata. We validate the method on images extracted from Flickr which contain both visual information and metadata and compare the results when using both, visual information only or metadata only. This work has been done in the context of the MediaEval Multimedia Satellite Task

Incremento de la temperatura en el punto de fusión de bioceras producidas por hidrotratamiento de aceite de palma usando cristalización sin solvente

Currently, different waxes are being developed by hydrogenation of palm oil. However, the products obtained do not reach the melting point and hardness conditions required cosmetic industry. Therefore, this article evaluated a solvent-free crystallization process to increase the melting temperature and the mass yields of the bio-wax required to meet the market specifications. The operating temperature range was determined by DSC and X-ray diffraction. Once the crystallization points were known, the sample was heated to 50 °C and then cooled to 40 °C for bio-wax 1 and 30 °C for bio-wax 2; at this temperature two fractions (liquid and solid) were obtained. Following this procedure, a solid fraction was obtained with an increase in the melting point from 47 °C to 49° C for bio-wax 1 and from 45 °C to 47 °C for bio-wax 2. The crystallization process does not separate the different families of compounds in the bio-wax fractions, so there are not significant changes in the acidity, saponification and iodine index parameters. Future research on other complementary refining techniques such as neutralization and bleaching, will allow bio-wax reach international quality criteria for cosmetic and pharmaceutical industries.actualmente, se están produciendo ceras por hidrotratamiento de aceite de palma, sin embargo, los productos obtenidos no siempre alcanzan las condiciones de punto de fusión y dureza requeridas por la industria cosmética. Debido a esta problemática, el interés de este trabajo fue evaluar un proceso de cristalización sin solvente con el objetivo de incrementar la temperatura de fusión y los rendimientos másicos de la biocera, acercándolos a los intervalos de los valores de las propiedades fisicoquímicas que requiere el mercado. Para cumplir con este propósito, se determinó el rango operativo de la temperatura de cristalización por calorimetría diferencial de barrido (dsc) y difracción de rayos X (drx). Una vez conocidos los puntos de cristalización, se procedió a calentar la muestra hasta los 50 °C y seguidamente se realizó un enfriamiento controlado hasta que la biocera 1 alcanzara los 40 °C para y de 30 °C para la biocera 2, y a estas temperaturas se consiguieron dos fracciones (líquida y sólida). Las fracciones sólidas presentaron un incremento en la temperatura de fusión de 47 °C a 49 °C para la biocera 1, y de 45 ºC a 47 ºC para la biocera 2. El proceso de cristalización no separó las diferentes familias de compuestos presentes en las bioceras, por lo que no se evidenciaron cambios significativos en los parámetros índice de acidez, saponificación y yodo. Además, futuras investigaciones en otras técnicas de refinación complementarias a la cristalización, como neutralización y decoloración, permitirán a la biocera de palma cumplir con los criterios de calidad internacionales para ser utilizada como ingrediente en las industrias cosmética y farmacéutica

What to consider when pseudohypoparathyroidism is ruled out: IPPSD and differential diagnosis

Background: Pseudohypoparathyroidism (PHP) is a rare disease whose phenotypic features are rather difficult to identify in some cases. Thus, although these patients may present with the Albright''s hereditary osteodystrophy (AHO) phenotype, which is characterized by small stature, obesity with a rounded face, subcutaneous ossifications, mental retardation and brachydactyly, its manifestations are somewhat variable. Indeed, some of them present with a complete phenotype, whereas others show only subtle manifestations. In addition, the features of the AHO phenotype are not specific to it and a similar phenotype is also commonly observed in other syndromes. Brachydactyly type E (BDE) is the most specific and objective feature of the AHO phenotype, and several genes have been associated with syndromic BDE in the past few years. Moreover, these syndromes have a skeletal and endocrinological phenotype that overlaps with AHO/PHP. In light of the above, we have developed an algorithm to aid in genetic testing of patients with clinical features of AHO but with no causative molecular defect at the GNAS locus. Starting with the feature of brachydactyly, this algorithm allows the differential diagnosis to be broadened and, with the addition of other clinical features, can guide genetic testing. Methods: We reviewed our series of patients (n = 23) with a clinical diagnosis of AHO and with brachydactyly type E or similar pattern, who were negative for GNAS anomalies, and classify them according to the diagnosis algorithm to finally propose and analyse the most probable gene(s) in each case. Results: A review of the clinical data for our series of patients, and subsequent analysis of the candidate gene(s), allowed detection of the underlying molecular defect in 12 out of 23 patients: five patients harboured a mutation in PRKAR1A, one in PDE4D, four in TRPS1 and two in PTHLH. Conclusions: This study confirmed that the screening of other genes implicated in syndromes with BDE and AHO or a similar phenotype is very helpful for establishing a correct genetic diagnosis for those patients who have been misdiagnosed with "AHO-like phenotype" with an unknown genetic cause, and also for better describing the characteristic and differential features of these less common syndromes

Fields with no recent legume cultivation have sufficient nitrogen-fixing rhizobia for crops of faba bean (Vicia faba L.)

Purpose (1) To assess the biological N fixation (BNF) potential of varieties of faba bean (Vicia faba L.) cropped with or without compost in an experimental field-scale rotation with no recent history of legumes, (2) to enumerate soil populations of Rhizobium leguminosarum sv. viciae (Rlv), and to genetically characterize the nodulating Rlv strains, (3) compare BNF with other sites in Britain. Methods BNF was evaluated from 2012–2015 using 15N natural abundance. Treatments were either PK fertilizer or compost. Soil rhizobial populations were determined using qPCR, the symbiotic rhizobia genotyped (16S rRNA, nodA and nodD genes), and their BNF capacity assessed ex situ. The reliance of legumes on BNF at other British sites was estimated in a single season, and their nodulating symbionts examined. Results Faba bean obtained most of its N through BNF (>80%) regardless of variety or year. N-accumulation by cvs Babylon and Boxer increased with compost treatment in 2014/2015. Rhizobial populations were c. 105-106 Rlv cells g-1 soil regardless of field or treatment. 157 Rlv microsymbionts grouped into two large nodAD clades; one mainly from V. faba, and the other from various legumes. All isolates nodulated, and some performed better than commercial inoculant strains. Conclusions Faba bean can provide most of its nitrogen through BNF and leave economically valuable residual N for subsequent crops. Recent legume cropping in northern Europe is not essential for effective nodulation: rhizobia may persist in a range of farmland locations. Nevertheless, there is the potential to apply elite rhizobial strains as inoculants in some soils