Development of an Analytic Nodal Diffusion Solver in Multigroups for 3D Reactor Cores with Rectangular or Hexagonal Assemblies.

More accurate modelling of physical phenomena involved in present and future nuclear reactors requires a multi-scale and multi-physics approach. This challenge can be accomplished by the coupling of best-estimate core-physics, thermal-hydraulics and multi-physics solvers. In order to make viable that coupling, the current trends in reactor simulations are along the development of a new generation of tools based on user-friendly, modular, easily linkable, faster and more accurate codes to be integrated in common platforms. These premises are in the origin of the NURESIM Integrated Project within the 6th European Framework Program, which is envisaged to provide the initial step towards a Common European Standard Software Platform for nuclear reactors simulations. In the frame of this project and to reach the above-mentioned goals, a 3-D multigroup nodal solver for neutron diffusion calculations called ANDES (Analytic Nodal Diffusion Equation Solver) has been developed and tested in-depth in this Thesis. ANDES solves the steady-state and time-dependent neutron diffusion equation in threedimensions and any number of energy groups, utilizing the Analytic Coarse-Mesh Finite-Difference (ACMFD) scheme to yield the nodal coupling equations. It can be applied to both Cartesian and triangular-Z geometries, so that simulations of LWR as well as VVER, HTR and fast reactors can be performed. The solver has been implemented in a fully encapsulated way, enabling it as a module to be readily integrated in other codes and platforms. In fact, it can be used either as a stand-alone nodal code or as a solver to accelerate the convergence of whole core pin-by-pin code systems. Verification of performance has shown that ANDES is a code with high order definition for whole core realistic nodal simulations. In this paper, the methodology developed and involved in ANDES is presented

Análisis de la transmutación de Actínidos Minoritarios en un reactor rápido de sodio con modelo de carga homogéneo mediante el código MCNPX-CINDER

El reactor rápido refrigerado por sodio (SFR) constituye uno de los conceptos más prometedores de los seis considerados en la Generación IV de reactores nucleares, encontrándose actualmente en fase de investigación. En este marco surge el proyecto europeo CP ESFR (Collaborative Project for an European Sodium Fast Reactor) cuya finalidad es analizar los diversos desafíos y oportunidades que el desarrollo de este tipo de reactores plantea, ya sea en términos de seguridad, tecnología de sodio, capacidades transmutadoras, etc

The analytic nodal diffusion solver ANDES in multigroups for 3D rectangular geometry: Development and performance analysis

In this work we address the development and implementation of the analytic coarse-mesh finite-difference (ACMFD) method in a nodal neutron diffusion solver called ANDES. The first version of the solver is implemented in any number of neutron energy groups, and in 3D Cartesian geometries; thus it mainly addresses PWR and BWR core simulations. The details about the generalization to multigroups and 3D, as well as the implementation of the method are given. The transverse integration procedure is the scheme chosen to extend the ACMFD formulation to multidimensional problems. The role of the transverse leakage treatment in the accuracy of the nodal solutions is analyzed in detail: the involved assumptions, the limitations of the method in terms of nodal width, the alternative approaches to implement the transverse leakage terms in nodal methods – implicit or explicit _, and the error assessment due to transverse integration. A new approach for solving the control rod ‘‘cusping” problem, based on the direct application of the ACMFD method, is also developed and implemented in ANDES. The solver architecture turns ANDES into an user-friendly, modular and easily linkable tool, as required to be integrated into common software platforms for multi-scale and multi-physics simulations. ANDES can be used either as a stand-alone nodal code or as a solver to accelerate the convergence of whole core pin-by-pin code systems. The verification and performance of the solver are demonstrated using both proof-of-principle test cases and well-referenced international benchmarks

Transient analysis in the 3D nodal kinetics and thermal-hydraulics ANDES/COBRA coupled system

Neutron kinetics has been implemented in the 3D nodal solver ANDES, which has been coupled to the core thermal-hydraulics (TH) code COBRA-III for core transient analysis. The purpose of this work is, first, to discuss and test the ability of the kinetics solver ANDES to model transients; and second, by means of a systematic analysis, including alternate kinetics schemes, time step size, nodal size, neutron energy groups and spectrum, to serve as a basis for the development of more accurate and efficient neutronics/thermal-hydraulics tools for general transient simulations. The PWR MOX/UO2 transient benchmark provided by the OECD/NEA and US NRC was selected for these goals. The obtained ANDES/COBRA-III results were consistent with other solutions to the benchmark; the differences in the TH feedback led to slight differences in the core power evolution, whereas very good agreements were found in the other requested parameters. The performed systematic analysis highlighted the optimum kinetics iterative scheme, and showed that neutronics spatial discretization effects have stronger influence than time discretization effects, in the semi-implicit scheme adopted, on the numerical solution. On the other hand, the number of energy groups has an important influence on the transient evolution, whereas the assumption of using the prompt neutron spectrum for delayed neutrons is acceptable as it leads to small relative errors

Extension of the analytic nodal diffusion solver ANDES to triangular-Z geometry and coupling with COBRA-IIIc for hexagonal core analysis.

In this paper the extension of the multigroup nodal diffusion code ANDES, based on the Analytic Coarse Mesh Finite Difference (ACMFD) method, from Cartesian to hexagonal geometry is presented, as well as its coupling with the thermal–hydraulic (TH) code COBRA-IIIc for hexagonal core analysis. In extending the ACMFD method to hexagonal assemblies, triangular-Z nodes are used. In the radial plane, a direct transverse integration procedure is applied along the three directions that are orthogonal to the triangle interfaces. The triangular nodalization avoids the singularities, that appear when applying transverse integration to hexagonal nodes, and allows the advantage of the mesh subdivision capabilities implicit within that geometry. As for the thermal–hydraulics, the extension of the coupling scheme to hexagonal geometry has been performed with the capability to model the core using either assembly-wise channels (hexagonal mesh) or a higher refinement with six channels per fuel assembly (triangular mesh). Achieving this level of TH mesh refinement with COBRA-IIIc code provides a better estimation of the in-core 3D flow distribution, improving the TH core modelling. The neutronics and thermal–hydraulics coupled code, ANDES/COBRA-IIIc, previously verified in Cartesian geometry core analysis, can also be applied now to full three-dimensional VVER core problems, as well as to other thermal and fast hexagonal core designs. Verification results are provided, corresponding to the different cases of the OECD/NEA-NSC VVER-1000 Coolant Transient Benchmarks

Clinical text classification in Cancer Real-World Data in Spanish

Healthcare systems currently store a large amount of clinical data, mostly unstructured textual information, such as electronic health records (EHRs). Manually extracting valuable information from these documents is costly for healthcare professionals. For example, when a patient first arrives at an oncology clinical analysis unit, clinical staff must extract information about the type of neoplasm in order to assign the appropriate clinical specialist. Automating this task is equivalent to text classification in natural language processing (NLP). In this study, we have attempted to extract the neoplasm type by processing Spanish clinical documents. A private corpus of 23, 704 real clinical cases has been processed to extract the three most common types of neoplasms in the Spanish territory: breast, lung and colorectal neoplasms. We have developed methodologies based on state-of-the-art text classification task, strategies based on machine learning and bag-of-words, based on embedding models in a supervised task, and based on bidirectional recurrent neural networks with convolutional layers (C-BiRNN). The results obtained show that the application of NLP methods is extremely helpful in performing the task of neoplasm type extraction. In particular, the 2-BiGRU model with convolutional layer and pre-trained fastText embedding obtained the best performance, with a macro-average, more representative than the micro-average due to the unbalanced data, of 0.981 for precision, 0.984 for recall and 0.982 for F1-score.The authors acknowledge the support from the Ministerio de Ciencia e Innovación (MICINN) under project PID2020-116898RB-I00, from Universidad de Málaga and Junta de Andalucía through grants UMA20-FEDERJA-045 and PYC20-046-UMA (all including FEDER funds), and from the Malaga-Pfizer consortium for AI research in Cancer - MAPIC. Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Explainable clinical coding with in-domain adapted transformers

Background and Objective: Automatic clinical coding is a crucial task in the process of extracting relevant in-formation from unstructured medical documents contained in Electronic Health Records (EHR). However, most of the existing computer-based methods for clinical coding act as “black boxes”, without giving a detailed description of the reasons for the clinical-coding assignments, which greatly limits their applicability to real-world medical scenarios. The objective of this study is to use transformer-based models to effectively tackle explainable clinical-coding. In this way, we require the models to perform the assignments of clinical codes to medical cases, but also to provide the reference in the text that justifies each coding assignment. Methods: We examine the performance of 3 transformer-based architectures on 3 different explainable clinical-coding tasks. For each transformer, we compare the performance of the original general-domain version with an in-domain version of the model adapted to the specificities of the medical domain. We address the explainable clinical-coding problem as a dual medical named entity recognition (MER) and medical named entity normal-ization (MEN) task. For this purpose, we have developed two different approaches, namely a multi-task and a hierarchical-task strategy. Results: For each analyzed transformer, the clinical-domain version significantly outperforms the corresponding general domain model across the 3 explainable clinical-coding tasks analyzed in this study. Furthermore, the hierarchical-task approach yields a significantly superior performance than the multi-task strategy. Specifically, the combination of the hierarchical-task strategy with an ensemble approach leveraging the predictive capa-bilities of the 3 distinct clinical-domain transformersFunding for open access charge: Universidad de Málaga / CBUA. The authors thankfully acknowledge the computer resources, technical expertise and assistance provided by the SCBI (Supercomputing and Bioinformatics) center of the University of Málaga

La vigilancia en España 3 años después de la entrada en vigor de la Ley General de Salud Pública

ResumenEn 2014, el Grupo de Trabajo de Vigilancia Epidemiológica de la Sociedad Española de Epidemiología llevó a cabo un estudio descriptivo con el fin de evaluar el desarrollo de la Ley General de Salud Pública, promulgada en España en 2011. Se remitió una encuesta a las 19 comunidades y ciudades autónomas para evaluar la existencia de sistemas de información y otros aspectos de los distintos apartados de vigilancia incluidos en la ley. Todas disponían de un sistema de información para enfermedades transmisibles y en seis para condicionantes sociales; 18 vigilaban al menos una enfermedad crónica y 14 alguno de sus determinantes. El 100% analizaba sistemáticamente la información procedente de la vigilancia de las enfermedades transmisibles. Hay margen de mejora para la vigilancia de la salud pública en España. La acción debe ir dirigida a los principales problemas de salud.AbstractIn 2014, the Epidemiological Surveillance Working Group of the Sociedad Española de Epidemiología (Spanish Society of Epidemiology), carried out a descriptive study in order to evaluate the level of development of the Spanish Public Health Law since its enactment in 2011. A survey collecting data on the existence of information systems and other aspects pertaining to each surveillance section included in the law was sent to all 19 autonomous communities and cities. All regional authorities reported the presence of an information system for communicable diseases, and six also reported an information system for social factors. 18 reported that at least one chronic disease was subject to surveillance and 14 confirmed surveillance of some of its determinants. They all systematically analysed the data derived from the communicable diseases. There is room for improvement in Public Health surveillance in Spain, and action should be aimed at the main health problems

Description of industrial pollution in Spain

BACKGROUND: Toxic substances released into the environment (to both air and water) by many types of industries might be related with the occurrence of some malignant tumours and other diseases. The publication of the EPER (European Pollutant Emission Register) Spanish data allows to investigate the presence of geographical mortality patterns related to industrial pollution. The aim of this paper is to describe industrial air and water pollution in Spain in 2001, broken down by activity group and specific pollutant, and to plot maps depicting emissions of carcinogenic substances. METHODS: All information on industrial pollution discharge in 2001 was drawn from EPER-Spain public records provided by the European Commission server. We described the distribution of the number of industries and amounts discharged for each pollutant, as well as emission by pollutant group and the industrial activities associated with each pollutant. Maps of Spain were drawn up, with UTM coordinates being used to plot pollutant foci, and circles with an area proportional to the emission to depict pollution emission values. RESULTS: The EPER-Spain contained information on 1,437 industrial installations. The industrial plants that discharge pollutant substances into air and water above the pollutant-specific EPER threshold were mainly situated in the Autonomous Regions of Aragon, Andalusia and Catalonia and in Catalonia, the Basque Country and Andalusia respectively. Pollution released in 2001 into air approached 158 million Mt. Emissions into water were over 8 million Mt. CONCLUSION: A few single industrial plants are responsible for the highest percentage of emissions, thus rendering monitoring of their possible health impact on the surrounding population that much simpler. Among European countries Spain is the leading polluter in almost one third of all EPER-registered pollutant substances released into the air and ranks among the top three leading polluters in two-thirds of all such substances. Information obtained through publication of EPER data means that the possible consequences of reported pollutant foci on the health of neighbouring populations can now be studied

A Comparative Study on Feature Selection for a Risk Prediction Model for Colorectal Cancer

[EN]Background and objective: Risk prediction models aim at identifying people at higher risk of developing a target disease. Feature selection is particularly important to improve the prediction model performance avoiding overfitting and to identify the leading cancer risk (and protective) factors. Assessing the stability of feature selection/ranking algorithms becomes an important issue when the aim is to analyze the features with more prediction power. Methods: This work is focused on colorectal cancer, assessing several feature ranking algorithms in terms of performance for a set of risk prediction models (Neural Networks, Support Vector Machines (SVM), Logistic Regression, k-Nearest Neighbors and Boosted Trees). Additionally, their robustness is evaluated following a conventional approach with scalar stability metrics and a visual approach proposed in this work to study both similarity among feature ranking techniques as well as their individual stability. A comparative analysis is carried out between the most relevant features found out in this study and features provided by the experts according to the state-of-the-art knowledge. Results: The two best performance results in terms of Area Under the ROC Curve (AUC) are achieved with a SVM classifier using the top-41 features selected by the SVM wrapper approach (AUC=0.693) and Logistic Regression with the top-40 features selected by the Pearson (AUC=0.689). Experiments showed that performing feature selection contributes to classification performance with a 3.9% and 1.9% improvement in AUC for the SVM and Logistic Regression classifier, respectively, with respect to the results using the full feature set. The visual approach proposed in this work allows to see that the Neural Network-based wrapper ranking is the most unstable while the Random Forest is the most stable. Conclusions: This study demonstrates that stability and model performance should be studied jointly as Random Forest turned out to be the most stable algorithm but outperformed by others in terms of model performance while SVM wrapper and the Pearson correlation coefficient are moderately stable while achieving good model performance. © 2019 Elsevier B.V. All rights reservedS