Parallel local mesh refinement for Code Saturne

Computational Fluid Dynamics (CFD) is one of the eld which can fully utilize the capacity of existing HPC systems. There are many cases either from basic or applied research which are so complex that their numerical simulation with requested accuracy requires very ne representation of the computational domain. To solve certain problems numerical models consisting of hundred billions of cells are necessary. There are several approaches to create such huge meshes. One of them is based on global mesh re nement and is also known as mesh multiplication. This approach was already described in [1, 2]. Global re nement was already implemented into Code Saturne enhancing its capability in terms of mesh re nement. Meshes with sizes of up to one hundred billion of cells were generated on the y. Since there are many CFD problems where only local area is of interest (either areas close to boundaries, small geometrical entities or in regions with high gradient of solved quantities), local re nement is another approach for mesh creation. In this paper implementation of parallel local re nement applied to Code Saturne is described. The bottleneck of local adaptive re nement is that it breaks load balancing of the original mesh and requires a lot of global communications. Strategy to re-partition the mesh before its re nement is a key issue for optimal resource utilization. To minimize the amount of data transferred among cores it is necessary to do most of the communication during the preprocessing step on the coarse mesh before re nement. Local mesh re nement strategy was tested and its scalability and performance within Code Saturne were analysed. Results are presented in this paper

Advanced image processing methods for automatic liver segmentation

This paper presents advanced methods of image segmentation suitable for automatic recognition of the human liver and its vessel system, but in general could be used to segment any organ or body tissue. The comparison of studied methods is being made in terms of segmentation quality and algorithm speed. The main criterion for quality evaluation of each selected method is the level of conformity between the automatically recognized boundary and the reference boundary specified by experienced user. For all the tests sequences of CT and MRI images were used

Parameter identification of chaboche material model using indantation test data and inverse approach

In this paper genetic algorithm and sensitivity analysis are used to identify 6 parameters of Chaboche kinematic hardening model using repeated Finite element (FE) simulations of indentation test. Five of them are material constants of Chaboche kinematic hardening model itself. The last one represents the stiffness of the foundation and the indenter. To obtain experimental data indentation test under cyclic loading on universal tensile testing machine was performed. Because for sensitivity analysis to obtain all possible combinations of parameters and its values large number of simulation have to be performed supercomputer Anselm hosted by IT4Innovation has been used. Advantage of using supercomputer is that every simulation could use multiple cores which will reduce computational time. Moreover, since each simulation is independent, computational time could be further reduced by performing multiple simulations at the same time. It is clear from the comparison of both methods that the genetic algorithm is very good choice for the parameter estimation

D4.3 Benchmarking report as tested on the available infrastructure

The main focus of this deliverable is testing and benchmarking the available infrastructure using the execution frameworks PyCOMPSs and HyperLoom. A selected benchmark employing the Multi Level Monte Carlo (MLMC) algorithm was run on two systems: TIER-0 (MareNostrum4) and TIER-1 (Salomon) supercomputers. In both systems, good performance scalability was achieved

Professional quality of life and organizational changes: a five-year observational study in Primary Care

<p>Abstract</p> <p>Background</p> <p>The satisfaction and the quality of life perceived by professionals have implications for the performance of health organizations. We have assessed the variations in professional quality of life (PQL) and their explanatory factors during a services management decentralization process.</p> <p>Methods</p> <p>It was designed as a longitudinal analytical observational study in a Health Area in Madrid, Spain. Three surveys were sent out during an ongoing management decentralization process between 2001 and 2005. The professionals surveyed were divided into three groups: Group I (97.3% physicians), group II (92.5% nurses) and group III (auxiliary personnel). Analysis of the tendency and elaboration of an explanatory multivariate model was made. The PQL -35 questionnaire, based on Karasek's demand-control theory, was used to measure PQL. This questionnaire recognizes three PQL dimensions: management support (MS), workload (WL) and intrinsic motivation (IM).</p> <p>Results</p> <p>1444 responses were analyzed. PQL increased 0.16 (CI 95% 0.04 – 0.28) points in each survey. Group II presents over time a higher PQL score than group I of 0.38 (IC 95% 0.18 – 0.59) points. There is no difference between groups I and III.</p> <p>For each point that MS increases, PQL increases between 0.44 and 0.59 points. PQL decreases an average of between 0.35 and 0.49 point, for each point that WL increases.</p> <p>Age appears to have a marginal association with PQL (CI 95% 0.00 – 0.02), as it occurs with being single or not having a stable relationship (CI 95% 0.01 – 0.41). Performing management tasks currently or in the past is related to poorer PQL perception (CI 95% -0.45 – -0.06), and the same occurs with working other than morning shifts (CI 95% -0.03 – -0.40 points).</p> <p>PQL is not related to sex, location of the centre (rural/urban), time spent working in the organization or contractual situation.</p> <p>Conclusion</p> <p>With the improvement in work control and avoiding increases in workloads, PQL perception can be maintained despite deep organizational changes at the macro-management level. Different professional groups experience different perceptions depending on how the changes impact their position in the organization.</p

Design and baseline characteristics of the finerenone in reducing cardiovascular mortality and morbidity in diabetic kidney disease trial

Background: Among people with diabetes, those with kidney disease have exceptionally high rates of cardiovascular (CV) morbidity and mortality and progression of their underlying kidney disease. Finerenone is a novel, nonsteroidal, selective mineralocorticoid receptor antagonist that has shown to reduce albuminuria in type 2 diabetes (T2D) patients with chronic kidney disease (CKD) while revealing only a low risk of hyperkalemia. However, the effect of finerenone on CV and renal outcomes has not yet been investigated in long-term trials. Patients and Methods: The Finerenone in Reducing CV Mortality and Morbidity in Diabetic Kidney Disease (FIGARO-DKD) trial aims to assess the efficacy and safety of finerenone compared to placebo at reducing clinically important CV and renal outcomes in T2D patients with CKD. FIGARO-DKD is a randomized, double-blind, placebo-controlled, parallel-group, event-driven trial running in 47 countries with an expected duration of approximately 6 years. FIGARO-DKD randomized 7,437 patients with an estimated glomerular filtration rate >= 25 mL/min/1.73 m(2) and albuminuria (urinary albumin-to-creatinine ratio >= 30 to <= 5,000 mg/g). The study has at least 90% power to detect a 20% reduction in the risk of the primary outcome (overall two-sided significance level alpha = 0.05), the composite of time to first occurrence of CV death, nonfatal myocardial infarction, nonfatal stroke, or hospitalization for heart failure. Conclusions: FIGARO-DKD will determine whether an optimally treated cohort of T2D patients with CKD at high risk of CV and renal events will experience cardiorenal benefits with the addition of finerenone to their treatment regimen. Trial Registration: EudraCT number: 2015-000950-39; ClinicalTrials.gov identifier: NCT02545049

Advanced image processing methods for automatic liver segmentation

This paper presents advanced methods of image segmentation suitable for automatic recognition of the human liver and its vessel system, but in general could be used to segment any organ or body tissue. The comparison of studied methods is being made in terms of segmentation quality and algorithm speed. The main criterion for quality evaluation of each selected method is the level of conformity between the automatically recognized boundary and the reference boundary specified by experienced user. For all the tests sequences of CT and MRI images were used

Parameter identification of chaboche material model using indantation test data and inverse approach

In this paper genetic algorithm and sensitivity analysis are used to identify 6 parameters of Chaboche kinematic hardening model using repeated Finite element (FE) simulations of indentation test. Five of them are material constants of Chaboche kinematic hardening model itself. The last one represents the stiffness of the foundation and the indenter. To obtain experimental data indentation test under cyclic loading on universal tensile testing machine was performed. Because for sensitivity analysis to obtain all possible combinations of parameters and its values large number of simulation have to be performed supercomputer Anselm hosted by IT4Innovation has been used. Advantage of using supercomputer is that every simulation could use multiple cores which will reduce computational time. Moreover, since each simulation is independent, computational time could be further reduced by performing multiple simulations at the same time. It is clear from the comparison of both methods that the genetic algorithm is very good choice for the parameter estimation

Detection of Orbital Floor Fractures by Principal Component Analysis

Part 3: Images, Visualization, ClassificationInternational audiencePrincipal component analysis (PCA) is a statistical method based on orthogonal transformation, which is used to convert possibly correlated datasets into linearly uncorrelated variables called principal components. PCA is one of the simplest methods based on the eigenvector analysis. This method is widely used in many fields, such as signal processing, quality control or mechanical engineering. In this paper, we present the use of PCA in area of medical image processing. In the medical image processing with subsequent reconstruction of 3D models, data from sources such as Computed Tomography (CT) or Magnetic Resonance Imagining (MRI) are used. Series of images representing axial slices of human body are stored in Digital Imaging and Communications in Medicine (DICOM) format. Physical properties of different body tissues are characterized by different shades of grey of each pixel correlated to the tissue density. Properties of each pixel are then used in image segmentation and subsequent creation of 3D model of human organs. Image segmentation splits digital image into regions with similar properties which are later used to create 3D model. In many cases accurate detections of edges of such objects are necessary. This could be for example the case of a tumour or orbital fracture identification. In this paper, identification of the orbital fracture using PCA method is presented as an example of application of the method in the area of medical image processing