Contribution of externally bonded FRP shear reinforcement to the shear strength of RC beams

An experimental program of T-beams strengthened in shear by CFRP sheets has been developed with two longitudinal reinforcement ratios and different externally bonded CFRP shear strengthening amounts in a discontinuous or continuous U-shaped configuration with and without mechanical anchorages. The effectiveness of the external reinforcement without anchorages was low regardless the CFRP reinforcement ratio. There is an evidence of the interaction of the different shear strength components, concrete Vc, transverse steel Vs, CFRP sheets Vf that have been obtained from the registered data of the instrumentation. This interaction might explain that the ultimate shear force of some CFRP-strengthened beams was lower than the one of the control beam. There is a significant difference between the sum of the Vc, Vs, and Vf components and the total shear force. This difference is assumed to be carried out by the flange (including its internal transverse reinforcement), which represents a significant percentage of the total shear strength (with a mean value of 38%). Finally, the contribution of the CFRP has been obtained by different existing guidelines and has been compared to the experimental values.Peer ReviewedPostprint (author's final draft

The benefits of the school garden; a learning resource against childhood obesity

La obesidad ha sido asociada con múltiples factores de riesgo cardiovascular y una menor capacidad cognitiva en niños, repercutiendo negativamente en la adolescencia y en la edad adulta. Diversos estudios ponen de manifiesto que la adherencia a patrones alimentarios saludables podría ser un elemento clave para la prevención y reducción de la obesidad. En este trabajo se propone el uso del huerto escolar como recurso educativo. Además, se adjunta un ejemplo de recurso didáctico para Educación Infantil, basado en un libro viajero y titulado “la pera cocinera”, con el fin de inculcar hábitos alimentarios saludables desde edades tempranas.Obesity has been associated with cardiovascular risk and lower cognitive function in children, with adverse consequences during adolescence and adulthood. Many studies highlight that adherence to healthy dietary patterns could act as a key factor to prevent and reduce obesity. In this manuscript, we propose the use of the school garden as an educational resource. Moreover, this work shows an example of learning resource for infant education, based on the use of a “traveller book” entitled “the cook pear”, whose objective is to inculcate healthy dietary patterns from the early age

A new methodology to characterise the radar bright band using doppler spectral moments from vertically pointing radar observations

The detection and characterisation of the radar Bright Band (BB) are essential for many applications of weather radar quantitative precipitation estimates, such as heavy rainfall surveillance, hydrological modelling or numerical weather prediction data assimilation. This study presents a new technique to detect the radar BB levels (top, peak and bottom) for Doppler radar spectral moments from the vertically pointing radars applied here to a K-band radar, the MRR-Pro (Micro Rain Radar). The methodology includes signal and noise detection and dealiasing schemes to provide realistic vertical Doppler velocities of precipitating hydrometeors, subsequent calculation of Doppler moments and associated parameters and BB detection and characterisation. Retrieved BB properties are compared with the melting level provided by the MRR-Pro manufacturer software and also with the 0 °C levels for both dry-bulb temperature (freezing level) and wet-bulb temperature from co-located radio soundings in 39 days. In addition, a co-located Parsivel disdrometer is used to analyse the equivalent reflectivity of the lowest radar height bins confirming consistent results of the new signal and noise detection scheme. The processing methodology is coded in a Python program called RaProM-Pro which is freely available in the GitHub repository

Role of ctDNA in Breast Cancer

Circulating tumor DNA is DNA released by the tumor into the bloodstream. In breast cancer, it is used mainly in research or in clinical trials, but it will likely be used in routine clinical practice once certain issues have been worked out and methods of analysis have been improved and standardized. Breast cancer classification and treatment selection are now based on analysis of the tumor but circulating tumor DNA carries many features of the original tumor and can be analyzed from a simple, non-invasive blood extraction. Here, we review its potential role in early breast cancer (for screening, diagnosis, detection of minimal residual disease after surgery, follow up, and treatment) and in metastatic breast cancer (for the detection of mutations, prognosis and treatment). Breast cancer is currently classified by immunohistochemistry. However, technological advances in the detection of circulating tumor DNA (ctDNA) have made new options available for diagnosis, classification, biological knowledge, and treatment selection. Breast cancer is a heterogeneous disease and ctDNA can accurately reflect this heterogeneity, allowing us to detect, monitor, and understand the evolution of the disease. Breast cancer patients have higher levels of circulating DNA than healthy subjects, and ctDNA can be used for different objectives at different timepoints of the disease, ranging from screening and early detection to monitoring for resistance mutations in advanced disease. In early breast cancer, ctDNA clearance has been associated with higher rates of complete pathological response after neoadjuvant treatment and with fewer recurrences after radical treatments. In metastatic disease, ctDNA can help select the optimal sequencing of treatments. In the future, thanks to new bioinformatics tools, the use of ctDNA in breast cancer will become more frequent, enhancing our knowledge of the biology of tumors. Moreover, deep learning algorithms may also be able to predict breast cancer evolution or treatment sensitivity. In the coming years, continued research and the improvement of liquid biopsy techniques will be key to the implementation of ctDNA analysis in routine clinical practice

Decoupling between precipitation processes and mountain wave induced circulations observed with a vertically pointing K-Band Doppler radar

Recent studies reported that precipitation and mountain waves induced low tropospheric level circulations may be decoupled or masked by greater spatial scale variability despite generally there is a connection between microphysical processes of precipitation and mountain driven air flows. In this paper we analyse two periods of a winter storm in the Eastern Pyrenees mountain range (NE Spain) with different mountain wave induced circulations and low-level turbulence as revealed by Micro Rain Radar (MRR), microwave radiometer and Parsivel disdrometer data during the Cerdanya-2017 field campaign. We find that during the event studied mountain wave wind circulations and low-level turbulence do not affect neither the snow crystal riming or aggregation along the vertical column nor the surface particle size distribution of the snow. This study illustrates that precipitation profiles and mountain induced circulations may be decoupled which can be very relevant for either ground-based or spaceborne remote sensing of precipitation

Precipitation type classification of micro rain radar data using an improved doppler spectral processing methodology

This research was funded by the Spanish Government through projects CGL2015-65627-C3-1-R, CGL2015-65627-C3-2-R (MINECO/FEDER), CGL2016-81828-REDT and RTI2018-098693-B-C32 (AEI/FEDER).This paper describes a methodology for processing spectral raw data from Micro Rain Radar (MRR), a K-band vertically pointing Doppler radar designed to observe precipitation profiles. The objective is to provide a set of radar integral parameters and derived variables, including a precipitation type classification. The methodology first includes an improved noise level determination, peak signal detection and Doppler dealiasing, allowing us to consider the upward movements of precipitation particles. A second step computes for each of the height bin radar moments, such as equivalent reflectivity (Ze), average Doppler vertical speed (W), spectral width (σ), the skewness and kurtosis. A third step performs a precipitation type classification for each bin height, considering snow, drizzle, rain, hail, and mixed (rain and snow or graupel). For liquid precipitation types, additional variables are computed, such as liquid water content (LWC), rain rate (RR), or gamma distribution parameters, such as the liquid water content normalized intercept (Nw) or the mean mass-weighted raindrop diameter (Dm) to classify stratiform or convective rainfall regimes. The methodology is applied to data recorded at the Eastern Pyrenees mountains (NE Spain), first with a detailed case study where results are compared with different instruments and, finally, with a 32-day analysis where the hydrometeor classification is compared with co-located Parsivel disdrometer precipitation-type present weather observations. The hydrometeor classification is evaluated with contingency table scores, including Probability of Detection (POD), False Alarm Rate (FAR), and Odds Ratio Skill Score (ORSS). The results indicate a very good capacity of Method3 to distinguish rainfall and snow (PODs equal or greater than 0.97), satisfactory results for mixed and drizzle (PODs of 0.79 and 0.69) and acceptable for a reduced number of hail cases (0.55), with relatively low rate of false alarms and good skill compared to random chance in all cases (FAR 0.70). The methodology is available as a Python language program called RaProM at the public github repository

Precipitation Type Classification of Micro Rain Radar Data Using an Improved Doppler Spectral Processing Methodology

This paper describes a methodology for processing spectral raw data from Micro Rain Radar (MRR), a K-band vertically pointing Doppler radar designed to observe precipitation profiles. The objective is to provide a set of radar integral parameters and derived variables, including a precipitation type classification. The methodology first includes an improved noise level determination, peak signal detection and Doppler dealiasing, allowing us to consider the upward movements of precipitation particles. A second step computes for each of the height bin radar moments, such as equivalent reflectivity (Ze), average Doppler vertical speed (W), spectral width (σ), the skewness and kurtosis. A third step performs a precipitation type classification for each bin height, considering snow, drizzle, rain, hail, and mixed (rain and snow or graupel). For liquid precipitation types, additional variables are computed, such as liquid water content (LWC), rain rate (RR), or gamma distribution parameters, such as the liquid water content normalized intercept (Nw) or the mean mass-weighted raindrop diameter (Dm) to classify stratiform or convective rainfall regimes. The methodology is applied to data recorded at the Eastern Pyrenees mountains (NE Spain), first with a detailed case study where results are compared with different instruments and, finally, with a 32-day analysis where the hydrometeor classification is compared with co-located Parsivel disdrometer precipitation-type present weather observations. The hydrometeor classification is evaluated with contingency table scores, including Probability of Detection (POD), False Alarm Rate (FAR), and Odds Ratio Skill Score (ORSS). The results indicate a very good capacity of Method3 to distinguish rainfall and snow (PODs equal or greater than 0.97), satisfactory results for mixed and drizzle (PODs of 0.79 and 0.69) and acceptable for a reduced number of hail cases (0.55), with relatively low rate of false alarms and good skill compared to random chance in all cases (FAR 0.70). The methodology is available as a Python language program called RaProM at the public github repository

Land Use and Topography Influence in a Complex Terrain Area: A High Resolution Mesoscale Modelling Study over the Eastern Pyrenees using the WRF Model

Different types of land use (LU) have different physical properties which can change local energy balance and hence vertical fluxes of moisture, heat and momentum. This in turn leads to changes in near-surface temperature and moisture fields. Simulating atmospheric flow over complex terrain requires accurate local-scale energy balance and therefore model grid spacing must be sufficient to represent both topography and land-use. In this study we use both the Corine Land Cover (CLC) and United States Geological Survey (USGS) land use databases for use with the Weather Research and Forecasting (WRF) model and evaluate the importance of both land-use classification and horizontal resolution in contributing to successful modelling of surface temperatures and humidities observed from a network of 39 sensors over a 9 day period in summer 2013. We examine case studies of the effects of thermal inertia and soil moisture availability at individual locations. The scale at which the LU classification is observed influences the success of the model in reproducing observed patterns of temperature and moisture. Statistical validation of model output demonstrates model sensitivity to both the choice of LU database used and the horizontal resolution. In general, results show that on average, by a) using CLC instead of USGS and/or b) increasing horizontal resolution, model performance is improved. We also show that the sensitivity to these changes in the model performance shows a daily cycle