Stabilized lowest order finite element approximation for linear three-field poroelasticity

A stabilized conforming mixed finite element method for the three-field (displacement, fluid flux and pressure) poroelasticity problem is developed and analyzed. We use the lowest possible approximation order, namely piecewise constant approximation for the pressure and piecewise linear continuous elements for the displacements and fluid flux. By applying a local pressure jump stabilization term to the mass conservation equation we ensure stability and avoid pressure oscillations. Importantly, the discretization leads to a symmetric linear system. For the fully discretized problem we prove existence and uniqueness, an energy estimate and an optimal a-priori error estimate, including an error estimate for the divergence of the fluid flux. Numerical experiments in 2D and 3D illustrate the convergence of the method, show the effectiveness of the method to overcome spurious pressure oscillations, and evaluate the added mass effect of the stabilization term.Comment: 25 page

A poroelastic model coupled to a fluid network with applications in lung modelling

Here we develop a lung ventilation model, based a continuum poroelastic representation of lung parenchyma and a 0D airway tree flow model. For the poroelastic approximation we design and implement a lowest order stabilised finite element method. This component is strongly coupled to the 0D airway tree model. The framework is applied to a realistic lung anatomical model derived from computed tomography data and an artificially generated airway tree to model the conducting airway region. Numerical simulations produce physiologically realistic solutions, and demonstrate the effect of airway constriction and reduced tissue elasticity on ventilation, tissue stress and alveolar pressure distribution. The key advantage of the model is the ability to provide insight into the mutual dependence between ventilation and deformation. This is essential when studying lung diseases, such as chronic obstructive pulmonary disease and pulmonary fibrosis. Thus the model can be used to form a better understanding of integrated lung mechanics in both the healthy and diseased states

Online railway delay management: Hardness, simulation and computation

Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG geförderten) Allianz- bzw. Nationallizenz frei zugänglich.This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.Delays in a railway network are a common problem that railway companies face in their daily operations. When a train is delayed, it may either be beneficial to let a connecting train wait so that passengers in the delayed train do not miss their connection, or it may be beneficial to let the connecting train depart on time to avoid further delays. These decisions naturally depend on the global structure of the network, on the schedule, on the passenger routes and on the imposed delays. The railway delay management (RDM) problem (in a broad sense) is to decide which trains have to wait for connecting trains and which trains have to depart on time. The offline version (i.e. when all delays are known in advance) is already NP-hard for very special networks. In this paper we show that the online railway delay management (ORDM) problem is PSPACE-hard. This result justifies the need for a simulation approach to evaluate wait policies for ORDM. For this purpose we present TOPSU—RDM, a simulation platform for evaluating and comparing different heuristics for the ORDM problem with stochastic delays. Our novel approach is to separate the actual simulation and the program that implements the decision-making policy, thus enabling implementations of different heuristics to ‘‘compete’’ on the same instances and delay distributions. We also report on computational results indicating the worthiness of developing intelligent wait policies. For RDM and other logistic planning processes, it is our goal to bridge the gap between theoretical models, which are accessible to theoretical analysis, but are often too far away from practice, and the methods which are used in practice today, whose performance is almost impossible to measure.EU/FP6/021235-2/EU/Algorithms for Robust and on-line Railway optimisation: Improving the validity and reliability of large-scale systems/ARRIVA

NF-κB, iNOS, IL-6, and collagen 1 and 5 expression in healthy and keratoconus corneal fibroblasts after 0.1% riboflavin UV-A illumination

Purpose To analyze the effect of riboflavin UV-A illumination on mRNA and protein expression of healthy (HCFs) and keratoconus human corneal fibroblasts (KC-HCFs), concerning the inflammatory markers NF-κB, iNOS, IL-6, and collagen 1 and 5 (Col 1/Col 5). Methods Keratocytes were isolated from healthy (n = 3) and keratoconus (KC) corneas (n = 3) and were cultivated in basal medium with 5% fetal calf serum, which resulted in their transformation into human corneal fibroblasts (HCFs/KC-HCFs). Cells underwent 0.1% riboflavin UV-A illumination for 250 s (CXL). NF-κB, iNOS, IL-6, Col 1, and Col 5 expression was investigated by qPCR and Western blot analysis. IL-6 concentration of the cell culture supernatant and cell lysate was determined by ELISA. Results In untreated KC-HCFs, NF-κB (p = 0.0002), iNOS (p = 0.0019), Col 1 (p = 0.0286), and Col 5 (p = 0.0054) mRNA expression was higher and IL-6 expression was lower (p = 0.0057), than in healthy controls. In HCFs, CXL led to an increased NF-κB (p = 0.0286) and IL-6 (p = 0.0057) mRNA expression. The IL-6 concentration in the cell culture supernatant was increased in HCFs (p = 0.0485) and KC-HCFs (p = 0.0485) after CXL. CXL increased intracellular IL-6 concentration only in KC-HCFs (p = 0.0357). In the HCF group (p = 0.0286), an increased Col 1 mRNA expression after CXL could be observed. Conclusion Our study confirmed altered gene expression in untreated KC-HCFs compared to untreated HCFs. Riboflavin UV-A illumination affected gene expression only in HCFs. Increased IL-6 concentration in the cell culture supernatant and cell lysate indicate a secondary inflammatory response of HCFs and KC-HCFs to riboflavin UV-A illumination

Stent implantation and balloon angioplasty for treatment of branch pulmonary artery stenosis in children

Objectives: Comparison of the results of branch pulmonary artery stenosis treated with balloon angioplasty (BA) or stent implantation (SI) in children. Background: Branch pulmonary artery stenosis may be treated with BA or SI. Methods: We compared the results of 147 interventions of branch pulmonary artery stenosis in 87 children (median age 3.6 years). Patients were treated during 1989-2000 with BA and during 2001-2004 with SI. Primary endpoints were acute complications and reintervention during follow up. Secondary variables were age, vessel diameter increase, acute success rate, balloon/vessel diameter ratio, pulmonary artery hypoplasia indices, and procedure related factors. Results: The acute vessel diameter increase with BA (4.31 ± 1.98 vs. 7.15 ± 2.31 mm) and SI (3.71 ± 1.58 vs. 6.97 ± 2.68 mm) was significant within both groups (P < 0.001), but not between both groups. The reintervention rate was comparable between both groups, but median time to reintervention was shorter after SI in infants compared to BA. The balloon/vessel diameter ratio was on average higher in BA than the stent/vessel diameter ratio in SI (3.49 ± 2.16 vs. 2.42 ± 0.56; P < 0.05) and was a significant risk factor (P < 0.01) for the higher complication rate after BA (BA: 14.1% vs. SI: 4.8%). No mortality occurred in both groups. Conclusion: BA and SI are safe interventional catheter therapies of branch pulmonary artery stenosis. The immediate results of BA and SI are comparable. The higher complication rate after BA, especially in infants, was associated with a higher balloon/vessel diameter ratio. SI seems to be a safe permanent alternative with foreign material, but requires more reinterventions in infants due to its therapeutic strateg

Data compression in the presence of observational error correlations

Numerical weather prediction (NWP) models are moving towards km-scale (and smaller) resolutions in order to forecast high-impact weather. As the resolution of NWP models increase the need for high-resolution observations to constrain these models also increases. A major hurdle to the assimilation of dense observations in NWP is the presence of non-negligible observation error correlations (OECs). Despite the difficulty in estimating these error correlations, progress is being made, with centres around the world now explicitly accounting for OECs in a variety of observation types. This paper explores how to make efficient use of this potentially dramatic increase in the amount of data available for assimilation. In an idealised framework it is illustrated that as the length-scales of the OECs increase the scales that the analysis is most sensitive to the observations become smaller. This implies that a denser network of observations is more beneficial with increasing OEC length-scales. However, the computational and storage burden associated with such a dense network may not be feasible. To reduce the amount of data, a compression technique based on retaining the maximum information content of the observations can be used. When the OEC length-scales are large (in comparison to the prior error correlations), the data compression will select observations of the smaller scales for assimilation whilst throwing out the larger scale information. In this case it is shown that there is a discrepancy between the observations with the maximum information and those that minimise the analysis error variances. Experiments are performed using the Ensemble Kalman Filter and the Lorenz-1996 model, comparing different forms of data reduction. It is found that as the OEC length-scales increase the assimilation becomes more sensitive to the choice of data reduction technique

Interpretable Fully Convolutional Classification of Intrapapillary Capillary Loops for Real-Time Detection of Early Squamous Neoplasia

In this work, we have concentrated our efforts on the interpretability of classification results coming from a fully convolutional neural network. Motivated by the classification of oesophageal tissue for real-time detection of early squamous neoplasia, the most frequent kind of oesophageal cancer in Asia, we present a new dataset and a novel deep learning method that by means of deep supervision and a newly introduced concept, the embedded Class Activation Map (eCAM), focuses on the interpretability of results as a design constraint of a convolutional network. We present a new approach to visualise attention that aims to give some insights on those areas of the oesophageal tissue that lead a network to conclude that the images belong to a particular class and compare them with those visual features employed by clinicians to produce a clinical diagnosis. In comparison to a baseline method which does not feature deep supervision but provides attention by grafting Class Activation Maps, we improve the F1-score from 87.3% to 92.7% and provide more detailed attention maps