58 research outputs found

    Genetic variation for bulb size, soluble solids content and pungency in the Spanish sweet onion variety Fuentes de Ebro. Response to selection for low pungency

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    The cultivar ‘Fuentes de Ebro’ is a long-day onion grown in the northeast of Spain, which is characterized by its succulence and low pungency. However, to match the market demand the size, pungency, and storability need to be improved. We have evaluated these quality-related bulb traits in 15 grower’s open-pollinated lines of this cultivar. Phenotypic variation observed for bulb weight, size and soluble solids content was significantly affected by location, growing season and line, while pungency depended on line and plot location. We found higher levels of genetic variation for bulb size and pungency than for soluble solids content, and significant phenotypic correlations indicated that milder onion tend to show larger size and lower soluble solids content. After one cycle of selection, we have obtained progeny with significantly lower pungency levels, and therefore, we estimated a realized heritability of 0.67. As conclusion, it is feasible to obtain a sweet cultivar after some selection cycles in the ‘Fuentes de Ebro’ onion, although considering indirect consequences in size and soluble solids conten

    Tactical and operational management of wind energy systems with storage using a probabilistic forecast of the energy resource

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    The storage of energy facilitates the management of renewable energy systems by reducing the mismatch between the supplied energy and the forecasted production due to forecasting errors. The storage increases the reliability of the renewable energy system and enables participation in the electricity market by committing to the sale of electricity for the following day. Nevertheless, the inclusion of the energy storage capacity requires the development of new management policies. In this paper, we propose a management strategy for a renewable energy system with storage capacity that integrates tactical and operational decisions in a single mathematical model that makes use of an updated probabilistic wind speed forecast. Management policies are obtained by solving a sequence of rolling-horizon stochastic optimization problems whose formulation is inspired by the Stochastic Approximation Average technique. The management policies are illustrated by their application to wind-farms using hydrogen as the energy storage medium

    New insights into Capsicum spp relatedness and the diversification process of Capsicum annuum in Spain

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    The successful exploitation of germplasm banks, harbouring plant genetic resources indispensable for plant breeding, will depend on our ability to characterize their genetic diversity. The Vegetable Germplasm Bank of Zaragoza (BGHZ) (Spain) holds an important Capsicum annuum collection, where most of the Spanish pepper variability is represented, as well as several accessions of other domesticated and non-domesticated Capsicum spp from all over the five continents. In the present work, a total of 51 C. annuum landraces (mainly from Spain) and 51 accessions from nine Capsicum species maintained at the BGHZ were evaluated using 39 microsatellite (SSR) markers spanning the whole genome. The 39 polymorphic markers allowed the detection of 381 alleles, with an average of 9.8 alleles per locus. A sizeable proportion of alleles (41.2%) were recorded as specific alleles and the majority of these were present at very low frequencies (rare alleles). Multivariate and model-based analyses partitioned the collection in seven clusters comprising the ten different Capsicum spp analysed: C. annuum, C. chinense, C. frutescens, C. pubescens, C. bacatum, C. chacoense and C. eximium. The data clearly showed the close relationships between C. chinense and C. frutescens. C. cardenasii and C. eximium were indistinguishable as a single, morphologically variable species. Moreover, C. chacoense was placed between C. baccatum and C. pubescens complexes. The C. annuum group was structured into three main clusters, mostly according to the pepper fruit shape, size and potential pungency. Results suggest that the diversification of C. annuum in Spain may occur from a rather limited gene pool, still represented by few landraces with ancestral traits. This ancient population would suffer from local selection at the distinct geographical regions of Spain, giving way to pungent and elongated fruited peppers in the South and Center, while sweet blocky and triangular types in Northern Spain

    In situ visualization of large-scale turbulence simulations in Nek5000 with ParaView Catalyst

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    In situ visualization on high-performance computing systems allows us to analyze simulation results that would otherwise be impossible, given the size of the simulation data sets and offline post-processing execution time. We develop an in situ adaptor for Paraview Catalyst and Nek5000, a massively parallel Fortran and C code for computational fluid dynamics. We perform a strong scalability test up to 2048 cores on KTH’s Beskow Cray XC40 supercomputer and assess in situ visualization’s impact on the Nek5000 performance. In our study case, a high-fidelity simulation of turbulent flow, we observe that in situ operations significantly limit the strong scalability of the code, reducing the relative parallel efficiency to only ≈ 21 % on 2048 cores (the relative efficiency of Nek5000 without in situ operations is ≈ 99 %). Through profiling with Arm MAP, we identified a bottleneck in the image composition step (that uses the Radix-kr algorithm) where a majority of the time is spent on MPI communication. We also identified an imbalance of in situ processing time between rank 0 and all other ranks. In our case, better scaling and load-balancing in the parallel image composition would considerably improve the performance of Nek5000 with in situ capabilities. In general, the result of this study highlights the technical challenges posed by the integration of high-performance simulation codes and data-analysis libraries and their practical use in complex cases, even when efficient algorithms already exist for a certain application scenario

    Predicting no-show medical appointments using machine learning

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    Health care centers face many issues due to the limited availability of resources, such as funds, equipment, beds, physicians, and nurses. Appointment absences lead to a waste of hospital resources as well as endangering patient health. This fact makes unattended medi- cal appointments both socially expensive and economically costly. This research aimed to build a predictive model to identify whether an appointment would be a no-show or not in order to reduce its consequences. This paper proposes a multi-stage framework to build an accurate predictor that also tackles the imbalanced property that the data exhibits. The first stage includes dimensionality reduction to compress the data into its most important components. The second stage deals with the imbalanced nature of the data. Different machine learning algorithms were used to build the classifiers in the third stage. Various evaluation metrics are also discussed and an evaluation scheme that fits the problem at hand is described. The work presented in this paper will help decision makers at health care centers to implement effective strategies to reduce the number of no-shows
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