Combined cerebellar and bilateral cervical posterior spinal artery stroke demonstrated on MRI

Combined cerebellar and spinal ischemic stroke is a rare, critical condition. We report a patient with combined cerebellar and bilateral posterolateral cervical spinal cord infarction due to bilateral stenosis of the vertebral arteries. MRI is the method of choice for imaging this condition; diffusion-weighted imaging of the spinal cord gives reliable results

Generalized drift-diffusion model for miniband superlattices

A drift-diffusion model of miniband transport in strongly coupled superlattices is derived from the single-miniband Boltzmann-Poisson transport equation with a BGK (Bhatnagar-Gross-Krook) collision term. We use a consistent Chapman-Enskog method to analyze the hyperbolic limit, at which collision and electric field terms dominate the other terms in the Boltzmann equation. The reduced equation is of the drift-diffusion type, but it includes additional terms, and diffusion and drift do not obey the Einstein relation except in the limit of high temperatures.Comment: 4 pages, 3 figures, double-column revtex. To appear as RC in PR

Assessment of evolutionary algorithms for optimal operating rules design in real Water Resource Systems

Two evolutionary algorithms (EAs) are assessed in this paper to design optimal operating rules (ORs) for Water Resource Systems (WRS). The assessment is established through a parameter analysis of both algorithms in a theoretical case, and the methodology described in this paper is applied to a complex, real case. These two applications allow us to analyse an algorithm's properties and performance by defining ORs, how an algorithm's termination/convergence criteria affect the results and the importance of decision-makers participating in the optimisation process. The former analysis reflects the need for correctly defining the important algorithm parameters to ensure an optimal result and how the greater number of termination conditions makes the algorithm an efficient tool for obtaining optimal ORs in less time. Finally, in the complex real case application, we discuss the participation value of decision-makers toward correctly defining the objectives and making decisions in the post-process. (C) 2014 Elsevier Ltd. All rights reserved.[EN]The authors wish to thank the University of Cagliari (Sardinia) and the Basin Agency of Sardinia for the data provided in the development of this study, as well as the Autonomous Region of Sardinia for funding the research project CRP 2_716. Thanks are also due to the Spanish Ministry of Science and Innovation (Comision Interministerial de Ciencia y Tecnologia, CICYT) for funding the projects NUTEGES (VI Plan Nacional de I+D-Ei 2008-2011, CGL2012-34978) and SCARCE (program Consolider-Ingenio 2010, project CSD2009-00065). The authors also thank the European Commission (Directorate-General for Research and Innovation) for funding the projects DROUGHT-R&SPI (program FP7-ENV-2011, project 282769), SIRIUS (FP7-SPACE-2010-1, project 262902), LIFE ALBUFERA project (LIFE12 ENV/ES/000685) and WAMCD project (EC-DG Environment No. 07.0329/2013/671291/SUB/ENV.C.1).Lerma Elvira, N.; Paredes Arquiola, J.; Andreu Álvarez, J.; Solera Solera, A.; Sechi, GM. (2015). Assessment of evolutionary algorithms for optimal operating rules design in real Water Resource Systems. Environmental Modelling and Software. 69:425-436. https://doi.org/10.1016/j.envsoft.2014.09.024S4254366

Bias adjustment and ensemble recalibration methods for seasonal forecasting: a comprehensive intercomparison using the C3S dataset

This work presents a comprehensive intercomparison of diferent alternatives for the calibration of seasonal forecasts, ranging from simple bias adjustment (BA)-e.g. quantile mapping-to more sophisticated ensemble recalibration (RC) methods- e.g. non-homogeneous Gaussian regression, which build on the temporal correspondence between the climate model and the corresponding observations to generate reliable predictions. To be as critical as possible, we validate the raw model and the calibrated forecasts in terms of a number of metrics which take into account diferent aspects of forecast quality (association, accuracy, discrimination and reliability). We focus on one-month lead forecasts of precipitation and temperature from four state-of-the-art seasonal forecasting systems, three of them included in the Copernicus Climate Change Service dataset (ECMWF-SEAS5, UK Met Ofce-GloSea5 and Météo France-System5) for boreal winter and summer over two illustrative regions with diferent skill characteristics (Europe and Southeast Asia). Our results indicate that both BA and RC methods efectively correct the large raw model biases, which is of paramount importance for users, particularly when directly using the climate model outputs to run impact models, or when computing climate indices depending on absolute values/thresholds. However, except for particular regions and/or seasons (typically with high skill), there is only marginal added value-with respect to the raw model outputs-beyond this bias removal. For those cases, RC methods can outperform BA ones, mostly due to an improvement in reliability. Finally, we also show that whereas an increase in the number of members only modestly afects the results obtained from calibration, longer hindcast periods lead to improved forecast quality, particularly for RC methods.This work has been funded by the C3S activity on Evaluation and Quality Control for seasonal forecasts. JMG was partially supported by the project MULTI-SDM (CGL2015-66583-R, MINECO/FEDER). FJDR was partially funded by the H2020 EUCP project (GA 776613)

The European project FLOMIX-R: Fluid mixing and flow distribution inthe reactor circuit - Final summary report

The project was aimed at describing the mixing phenomena relevant for both safety analysis, particularly in steam line break and boron dilution scenarios, and mixing phenomena of interest for economical operation and the structural integrity. Measurement data from a set of mixing experiments, gained by using advanced measurement techniques with enhanced resolution in time and space help to improve the basic understanding of turbulent mixing and to provide data for Computational Fluid Dynamics (CFD) code validation. Slug mixing tests simulating the start-up of the first main circulation pump are performed with two 1:5 scaled facilities: The Rossendorf coolant mixing model ROCOM and the VATTENFALL test facility, modelling a German Konvoi type and a Westinghouse type three-loop PWR, respectively. Additional data on slug mixing in a VVER-1000 type reactor gained at a 1:5 scaled metal mock-up at EDO Gidropress are provided. Experimental results on mixing of fluids with density differences obtained at ROCOM and the FORTUM PTS test facility are made available. Concerning mixing phenomena of interest for operational issues and thermal fatigue, flow distribution data available from commissioning tests (Sizewell-B for PWRs, Loviisa and Paks for VVERs) are used together with the data from the ROCOM facility as a basis for the flow distribution studies. The test matrix on flow distribution and steady state mixing performed at ROCOM comprises experiments with various combinations of running pumps and various mass flow rates in the working loops. Computational fluid dynamics calculations are accomplished for selected experiments with two different CFD codes (CFX-5, FLUENT). Best practice guidelines (BPG) are applied in all CFD work when choosing computational grid, time step, turbulence models, modelling of internal geometry, boundary conditions, numerical schemes and convergence criteria. The BPG contain a set of systematic procedures for quantifying and reducing numerical errors. The knowledge of these numerical errors is a prerequisite for the proper judgement of model errors. The strategy of code validation based on the BPG and a matrix of CFD code validation calculations have been elaborated. Besides of the benchmark cases, additional experiments were calculated by new partners and observers, joining the project later. Based on the "best practice solutions", conclusions on the applicability of CFD for turbulent mixing problems in PWR were drawn and recommendations on CFD modelling were given. The high importance of proper grid generation was outlined. In general, second order discretization schemes should be used to minimise numerical diffusion. First order schemes can provide physically wrong results. With optimised "production meshes" reasonable results were obtained, but due to the complex geometry of the flow domains, no fully grid independent solutions were achieved. Therefore, with respect to turbulence models, no final conclusions can be given. However, first order turbulence models like K-e or SST K-w are suitable for momentum driven slug mixing. For buoyancy driven mixing (PTS scenarios), Reynolds stress models provide better results

The TIGGE project and its achievements

TIGGE was a major component of the THORPEX (The Observing System Research and Predictability Experiment) research program, whose aim is to accelerate improvements in forecasting high-impact weather. By providing ensemble prediction data from leading operational forecast centers, TIGGE has enhanced collaboration between the research and operational meteorological communities and enabled research studies on a wide range of topics. The paper covers the objective evaluation of the TIGGE data. For a range of forecast parameters, it is shown to be beneficial to combine ensembles from several data providers in a Multi-model Grand Ensemble. Alternative methods to correct systematic errors, including the use of reforecast data, are also discussed. TIGGE data have been used for a range of research studies on predictability and dynamical processes. Tropical cyclones are the most destructive weather systems in the world, and are a focus of multi-model ensemble research. Their extra-tropical transition also has a major impact on skill of mid-latitude forecasts. We also review how TIGGE has added to our understanding of the dynamics of extra-tropical cyclones and storm tracks. Although TIGGE is a research project, it has proved invaluable for the development of products for future operational forecasting. Examples include the forecasting of tropical cyclone tracks, heavy rainfall, strong winds, and flood prediction through coupling hydrological models to ensembles. Finally the paper considers the legacy of TIGGE. We discuss the priorities and key issues in predictability and ensemble forecasting, including the new opportunities of convective-scale ensembles, links with ensemble data assimilation methods, and extension of the range of useful forecast skill

How to create an operational multi-model of seasonal forecasts?

Seasonal forecasts of variables like near-surface temperature or precipitation are becoming increasingly important for a wide range of stakeholders. Due to the many possibilities of recalibrating, combining, and verifying ensemble forecasts, there are ambiguities of which methods are most suitable. To address this we compare approaches how to process and verify multi-model seasonal forecasts based on a scientific assessment performed within the framework of the EU Copernicus Climate Change Service (C3S) Quality Assurance for Multi-model Seasonal Forecast Products (QA4Seas) contract C3S 51 lot 3. Our results underpin the importance of processing raw ensemble forecasts differently depending on the final forecast product needed. While ensemble forecasts benefit a lot from bias correction using climate conserving recalibration, this is not the case for the intrinsically bias adjusted multi-category probability forecasts. The same applies for multi-model combination. In this paper, we apply simple, but effective, approaches for multi-model combination of both forecast formats. Further, based on existing literature we recommend to use proper scoring rules like a sample version of the continuous ranked probability score and the ranked probability score for the verification of ensemble forecasts and multi-category probability forecasts, respectively. For a detailed global visualization of calibration as well as bias and dispersion errors, using the Chi-square decomposition of rank histograms proved to be appropriate for the analysis performed within QA4Seas.The research leading to these results is part of the Copernicus Climate Change Service (C3S) (Framework Agreement number C3S_51_Lot3_BSC), a program being implemented by the European Centre for Medium-Range Weather Forecasts (ECMWF) on behalf of the European Commission. Francisco Doblas-Reyes acknowledges the support by the H2020 EUCP project (GA 776613) and the MINECO-funded CLINSA project (CGL2017-85791-R)

Internet of Things for Environmental Sustainability and Climate Change

Our world is vulnerable to climate change risks such as glacier retreat, rising temperatures, more variable and intense weather events (e.g., floods, droughts, and frosts), deteriorating mountain ecosystems, soil degradation, and increasing water scarcity. However, there are big gaps in our understanding of changes in regional climate and how these changes will impact human and natural systems, making it difficult to anticipate, plan, and adapt to the coming changes. The IoT paradigm in this area can enhance our understanding of regional climate by using technology solutions, while providing the dynamic climate elements based on integrated environmental sensing and communications that is necessary to support climate change impacts assessments in each of the related areas (e.g., environmental quality and monitoring, sustainable energy, agricultural systems, cultural preservation, and sustainable mining). In the IoT in Environmental Sustainability and Climate Change chapter, a framework for informed creation, interpretation and use of climate change projections and for continued innovations in climate and environmental science driven by key societal and economic stakeholders is presented. In addition, the IoT cyberinfrastructure to support the development of continued innovations in climate and environmental science is discussed

A complementary study approach unravels novel players in the pathoetiology of Hirschsprung disease

Hirschsprung disease (HSCR, OMIM 142623) involves congenital intestinal obstruction caused by dysfunction of neural crest cells and their progeny during enteric nervous system (ENS) development. HSCR is a multifactorial disorder; pathogenetic variants accounting for disease phenotype are identified only in a minority of cases, and the identification of novel disease-relevant genes remains challenging. In order to identify and to validate a potential disease-causing relevance of novel HSCR candidate genes, we established a complementary study approach, combining whole exome sequencing (WES) with transcriptome analysis of murine embryonic ENS-related tissues, literature and databas