Sensitivity analysis of high-dimensional models with correlated inputs

Sensitivity analysis is an important tool used in many domains of computational science to either gain insight into the mathematical model and interaction of its parameters or study the uncertainty propagation through the input-output interactions. In many applications, the inputs are stochastically dependent, which violates one of the essential assumptions in the state-of-the-art sensitivity analysis methods. Consequently, the results obtained ignoring the correlations provide values which do not reflect the true contributions of the input parameters. This study proposes an approach to address the parameter correlations using a polynomial chaos expansion method and Rosenblatt and Cholesky transformations to reflect the parameter dependencies. Treatment of the correlated variables is discussed in context of variance and derivative-based sensitivity analysis. We demonstrate that the sensitivity of the correlated parameters can not only differ in magnitude, but even the sign of the derivative-based index can be inverted, thus significantly altering the model behavior compared to the prediction of the analysis disregarding the correlations. Numerous experiments are conducted using workflow automation tools within the VECMA toolkit

PSPIKE: A Parallel Hybrid Sparse Linear System Solver

The availability of large-scale computing platforms comprised of tens of thousands of multicore processors motivates the need for the next generation of highly scalable sparse linear system solvers. These solvers must optimize parallel performance, processor (serial) performance, as well as memory requirements, while being robust across broad classes of applications and systems. In this paper, we present a new parallel solver that combines the desirable characteristics of direct methods (robustness) and effective iterative solvers (low computational cost), while alleviating their drawbacks (memory requirements, lack of robustness). Our proposed hybrid solver is based on the general sparse solver PARDISO, and the “Spike” family of hybrid solvers. The resulting algorithm, called PSPIKE, is as robust as direct solvers, more reliable than classical preconditioned Krylov subspace methods, and much more scalable than direct sparse solvers. We support our performance and parallel scalability claims using detailed experimental studies and comparison with direct solvers, as well as classical preconditioned Krylov methods

Comparing simulation output accuracy of discrete event and agent based models: a quantitative approach

In our research we investigate the output accuracy of discrete event simulation models and agent based simulation models when studying human centric complex systems. In this paper we focus on human reactive behaviour as it is possible in both modelling approaches to implement human reactive behaviour in the model by using standard methods. As a case study we have chosen the retail sector, and here in particular the operations of the fitting room in the women wear department of a large UK department store. In our case study we looked at ways of determining the efficiency of implementing new management policies for the fitting room operation through modelling the reactive behaviour of staff and customers of the department. First, we have carried out a validation experiment in which we compared the results from our models to the performance of the real system. This experiment also allowed us to establish differences in output accuracy between the two modelling methods. In a second step a multi-scenario experiment was carried out to study the behaviour of the models when they are used for the purpose of operational improvement. Overall we have found that for our case study example both, discrete event simulation and agent based simulation have the same potential to support the investigation into the efficiency of implementing new management policies

The value of chest magnetic resonance imaging compared to chest radiographs with and without additional lung ultrasound in children with complicated pneumonia.

INTRODUCTION:In children with pneumonia, chest x-ray (CXR) is typically the first imaging modality used for diagnostic work-up. Repeated CXR or computed tomography (CT) are often necessary if complications such as abscesses or empyema arise, thus increasing radiation exposure. The aim of this retrospective study was to evaluate the potential of radiation-free chest magnetic resonance imaging (MRI) to detect complications at baseline and follow-up, compared to CXR with and without additional lung ultrasound (LUS). METHODS:Paired MRI and CXR scans were retrospectively reviewed by two blinded readers for presence and severity of pulmonary abscess, consolidation, bronchial wall thickening, mucus plugging and pleural effusion/empyema using a chest MRI scoring system. The scores for MRI and CXR were compared at baseline and follow-up. Furthermore, the MRI scores at baseline with and without contrast media were evaluated. RESULTS:33 pediatric patients (6.3±4.6 years), who had 33 paired MRI and CXR scans at baseline and 12 at follow-up were included. MRI detected significantly more lung abscess formations with a prevalence of 72.7% compared to 27.3% by CXR at baseline (p = 0.001), whereas CXR+LUS was nearly as good as MRI. MRI also showed a higher sensitivity in detecting empyema (p = 0.003). At follow-up, MRI also showed a slightly better sensitivity regarding residual abscesses. The overall severity of disease was rated higher on MRI. Contrast material did not improve detection of abscesses or empyema by MRI. CONCLUSION:CXR and LUS seem to be sufficient in most cases. In cases where LUS cannot be realized or the combination of CXR+LUS might be not sufficient, MRI, as a radiation free modality, should be preferred to CT. Furthermore, the admission of contrast media is not mandatory in this context

Magnetic resonance imaging detects onset and association with lung disease severity of bronchial artery dilatation in cystic fibrosis

Background Bronchial artery dilatation (BAD) is associated with haemoptysis in advanced cystic fibrosis (CF) lung disease. Our aim was to evaluate BAD onset and its association with disease severity by magnetic resonance imaging (MRI). Methods 188 CF patients (mean±sd age 13.8±10.6 years, range 1.1–55.2 years) underwent annual chest MRI (median three exams, range one to six exams), contributing a total of 485 MRI exams including perfusion MRI. Presence of BAD was evaluated by two radiologists in consensus. Disease severity was assessed using the validated MRI scoring system and spirometry (forced expiratory volume in 1 s (FEV1) % pred). Results MRI demonstrated BAD in 71 (37.8%) CF patients consistently from the first available exam and a further 10 (5.3%) patients first developed BAD during surveillance. Mean MRI global score in patients with BAD was 24.5±8.3 compared with 11.8±7.0 in patients without BAD (p<0.001) and FEV1 % pred was lower in patients with BAD compared with patients without BAD (60.8% versus 82.0%; p<0.001). BAD was more prevalent in patients with chronic Pseudomonas aeruginosa infection versus in patients without infection (63.6% versus 28.0%; p<0.001). In the 10 patients who newly developed BAD, the MRI global score increased from 15.1±7.8 before to 22.0±5.4 at first detection of BAD (p<0.05). Youden indices for the presence of BAD were 0.57 for age (cut-off 11.2 years), 0.65 for FEV1 % pred (cut-off 74.2%) and 0.62 for MRI global score (cut-off 15.5) (p<0.001). Conclusions MRI detects BAD in patients with CF without radiation exposure. Onset of BAD is associated with increased MRI scores, worse lung function and chronic P. aeruginosa infection, and may serve as a marker of disease severity