Efficiency of a gyroscopic device for conversion of mechanical wave energy to electrical energy:Technical report from ESGI-83 workshop in industrial mathematics 2011

We consider a recently proposed gyroscopic device for conversion of mechanical ocean wave energy to electrical energy. Two models of the device derived from standard engineering mechanics from the literature are analysed, and a model is derived from analytical mechanics considerations. From these models, estimates of the power production, efficiency, forces and moments are made. We find that it is possible to extract a significant amount of energy from an ocean wave using the described device. Further studies are required for a full treatment of the device.Resulting from the interaction with Joltech A/S at ESGI-83 (European Study Group with Industry) workshop on industrial mathematics, Sønderborg Denmark, 2011.</p

Discretizations in isogeometric analysis of Navier-Stokes flow

Abstract This paper deals with isogeometric analysis of the 2-dimensional, steady state, incompressible Navier-Stokes equation subjected to Dirichlet boundary conditions. We present a detailed description of the numerical method used to solve the boundary value problem. Numerical infsup stability tests for the simplified Stokes problem confirm the existence of many stable discretizations of the velocity and pressure spaces, and in particular show that stability may be achieved by means of knot refinement of the velocity space. Error convergence studies for the full Navier-Stokes problem show optimal convergence rates for this type of discretizations. Finally, a comparison of the results of the method to data from the literature for the the lid-driven square cavity for Reynolds numbers up to 10,000 serves as benchmarking of the discretizations and confirms the robustness of the method

Combining Polygenic and Proteomic Risk Scores With Clinical Risk Factors to Improve Performance for Diagnosing Absence of Coronary Artery Disease in Patients With De Novo Chest Pain

Background: Patients with de novo chest pain, referred for evaluation of possible coronary artery disease (CAD), frequently have an absence of CAD resulting in millions of tests not having any clinical impact. The objective of this study was to investigate whether polygenic risk scores and targeted proteomics improve the prediction of absence of CAD in patients with suspected CAD, when added to the PROMISE (Prospective Multicenter Imaging Study for Evaluation of Chest Pain) minimal risk score (PMRS). Methods: Genotyping and targeted plasma proteomics (N=368 proteins) were performed in 1440 patients with symptoms suspected to be caused by CAD undergoing coronary computed tomography angiography. Based on individual genotypes, a polygenic risk score for CAD (PRS CAD ) was calculated. The prediction was performed using combinations of PRS CAD , proteins, and PMRS as features in models using stability selection and machine learning. Results: Prediction of absence of CAD yielded an area under the curve of PRS CAD -model, 0.64±0.03; proteomic-model, 0.58±0.03; and PMRS model, 0.76±0.02. No significant correlation was found between the genetic and proteomic risk scores (Pearson correlation coefficient, −0.04; P =0.13). Optimal predictive ability was achieved by the full model (PRS CAD +protein+PMRS) yielding an area under the curve of 0.80±0.02 for absence of CAD, significantly better than the PMRS model alone ( P &lt;0.001). For reclassification purpose, the full model enabled down-classification of 49% (324 of 661) of the 5% to 15% pretest probability patients and 18% (113 of 611) of &gt;15% pretest probability patients. Conclusions: For patients with chest pain and low-intermediate CAD risk, incorporating targeted proteomics and polygenic risk scores into the risk assessment substantially improved the ability to predict the absence of CAD. Genetics and proteomics seem to add complementary information to the clinical risk factors and improve risk stratification in this large patient group. REGISTRATION: URL: https://www.clinicaltrials.gov ; Unique identifier: NCT0226471