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Zeolite-polymer composite materials manufactured in extrusion processes are generally revealing a dense structure and therefore a very poor accessibility of the incorporated zeolite particles. To improve and increase the accessibility of such composite materials, the present study aims at manufacturing of zeolite filled polymers by selective laser sintering. Focusing on the production of self-supporting and space optimized adsorber units, this manufacturing process could represent a key technology. Important bulk properties of the raw materials and the mixture were determined to derive manufacturing parameters. Various composite materials obtained by altering the zeolite content have favorable macroporous appearances which satisfy the need of highly accessible zeolitic micropores. As the structural integrity of the zeolite particles after the processing by selective laser sintering is an important aspect for maintaining of the adsorption capacity, the composite materials are analyzed by x-ray diffraction. Further, the water adsorption behavior of the composites is surveyed by their characteristic adsorption isotherms. [ABSTRACT FROM AUTHOR]

Fire blight is the most devastating bacterial disease of rosaceous plants. Forecasting fire blight infections is important to allow for countermeasures that reduce economic damage in pome fruit production. Current computerized forecasting models are solely based on physical factors such as temperature and moisture, but not on the actual presence of the pathogen Erwinia amylovora. Although the inoculum concentration is considered to be crucial for infection and disease outbreak, most current approaches used for identification of fire blight inoculum including morphological, biochemical, serological, and DNA-based methods are nonquantitative. Based on a real-time PCR approach previously published, an improved protocol to be used directly on whole bacteria in the field is described. The method allows for early detection and quantification of the pathogen prior to the occurrence of first symptoms. There is a clear correlation between bacterial abundance and subsequent disease development. However, in some cases, no disease symptoms could be observed despite a pathogen load of up to 3·4 × 106 cells per blossom. Integration of the amount of pathogen detected into refined prediction algorithms may allow for the improvement of applied forecasting models, finally permitting a better abatement of fire blight. [ABSTRACT FROM AUTHOR]

Abstract: An adaptive a posteriori error estimator based finite element method for the numerical solution of a coupled Cahn–Hilliard/Navier–Stokes system with a double-obstacle homogenous free (interfacial) energy density is proposed. A semi-implicit Euler scheme for the time-integration is applied which results in a system coupling a quasi-Stokes or Oseen-type problem for the fluid flow to a variational inequality for the concentration and the chemical potential according to the Cahn–Hilliard model [16]. A Moreau–Yosida regularization is employed which relaxes the constraints contained in the variational inequality and, thus, enables semi-smooth Newton solvers with locally superlinear convergence in function space. Moreover, upon discretization this yields a mesh independent method for a fixed relaxation parameter. For the finite dimensional approximation of the concentration and the chemical potential piecewise linear and globally continuous finite elements are used, and for the numerical approximation of the fluid velocity Taylor–Hood finite elements are employed. The paper ends by a report on numerical examples showing the efficiency of the new method. [Copyright &y& Elsevier]

In this paper we investigate a space-time finite element approximation of parabolic optimal control problems. The first order optimality conditions are transformed into an elliptic equation of fourth order in space and second order in time involving only the state or the adjoint state in the space-time domain. We derive a priori and a posteriori error estimates for the time discretization of the state and the adjoint state. Furthermore, we also propose a space-time mixed finite element discretization scheme to approximate the space-time elliptic equations, and derive a priori error estimates for the state and the adjoint state. Numerical examples are presented to illustrate our theoretical findings and the performance of our approach. [ABSTRACT FROM AUTHOR]

An adaptive finite-element semi-smooth Newton solver for the Cahn-Hilliard model with double obstacle free energy is proposed. For this purpose, the governing system is discretized in time using a semi-implicit scheme, and the resulting time-discrete system is formulated as an optimal control problem with pointwise constraints on the control. For the numerical solution of the optimal control problem, we propose a function space-based algorithm which combines a Moreau-Yosida regularization technique for handling the control constraints with a semi-smooth Newton method for solving the optimality systems of the resulting sub-problems. Further, for the discretization in space and in connection with the proposed algorithm, an adaptive finite-element method is considered. The performance of the overall algorithm is illustrated by numerical experiments. [ABSTRACT FROM AUTHOR]

We develop an adaptive finite element concept for elliptic optimal control problems with constraints on the state. In order to generate goal-oriented meshes, we extend the Dual Weighted Residual (DWR) concept proposed by Becker and Rannacher for PDE-constrained optimization to the state constrained case. Using the optimality system of the underlying PDE-constrained optimal control problem we obtain a representation for the error in the objectives. Based on this representation we define local error indicators, whose performance we investigate by means of a numerical example. [ABSTRACT FROM AUTHOR]

Abstract: Neuregulin (NRG)/ErbB receptor signaling pathways have recently been implicated in the reversal of long-term potentiation at hippocampal glutamatergic synapses. Moreover, polymorphisms in NRG-1 and ErbB-4 genes have been linked to an increased risk for developing schizophrenia. ErbB-4 is highly expressed at glutamatergic synapses where it binds to PSD-95 via its carboxyl terminal T–V–V sequence. Here we investigated the expression, localization and trafficking of ErbB-4 in cultured hippocampal neurons by immunocytochemistry, surface protein biotinylation, and live labeling of native receptors. We show that neuronal ErbB-4 is detected at its highest levels in GABAergic interneurons, as observed in vivo. ErbB-4 immunoreactivity precedes PSD-95 expression, with ErbB-4 cluster initially forming in the absence of, but later associating with, PSD-95-positive puncta. By surface protein biotinylation, the fraction of ErbB-4 receptors on the plasma membrane increases from 30% to 65% between 6 and 16 days in vitro (DIV). Interestingly, 30min of NRG stimulation triggers measurable ErbB-4 receptor internalization at DIV 16, despite increased colocalization with PSD-95. We also investigated the role of TNFα-converting enzyme (TACE)-mediated receptor processing in regulating ErbB-4 surface expression. We found that the cleavage-resistant JM-b isoform accounts for 80% of all ErbB-4 transcripts in cultured hippocampal neurons. Receptor stimulation or treatment with phorbol esters does not induce detectable ErbB-4 processing, indicating that neurons mostly rely on endocytosis of the intact receptor to regulate ErbB-4 surface expression. These results enhance our understanding of the regulation of ErbB-4—mediated signaling at glutamatergic synapses. [Copyright &y& Elsevier]

Second order methods for optimal semiconductor design based on the standard drift diffusion model are developed. Second—order necessary and sufficient conditions are established. Local quadratic convergence for the Newton method is proved. Numerical results for an unsymmetric (n—p) diode are presented. [ABSTRACT FROM AUTHOR]

SQP methods for the optimal control of the instationary Navier-Stokes equations with pointwise constraints on the control are considered. Due to the presence of the constraints, the quadratic subproblems (QPs) of SQP require a more sophisticated solver when compared to the unconstrained case. In this paper, a semismooth Newton method is proposed for efficiently solving the QPs. The convergence analysis, which is performed in an appropriate function space setting, relies on the concept of slant differentiability for proving locally superlinear convergence of the QP-solver. For the analysis of the outer SQP-iteration a generalized equations approach is utilized. Sufficient conditions for guaranteeing strong regularity of the generalized equation are established which, in turn, allows one to argue a quadratic rate of convergence of the SQP-method. The paper ends with a report on numerical results supporting the theoretical findings. [ABSTRACT FROM AUTHOR]

Based on the coupled problem of time-dependent fluid-structure interaction, equations for an appropriate adjoint problem are derived by the consequent use of the formal Lagrange calculus. Solutions of both primal and adjoint equations are computed in a partitioned fashion and enable the formulation of a surface sensitivity. This sensitivity is used in the context of a steepest descent algorithm for the computation of the required gradient of an appropriate cost functional. The efficiency of the developed optimization approach is demonstrated by minimization of the pressure drop in a simple two-dimensional channel flow and in a three-dimensional ducted flow surrounded by a thin-walled structure. [ABSTRACT FROM AUTHOR]

Parameters in mathematical models for glioblastoma multiforme (GBM) tumour growth are highly patient specific. Here, we aim to estimate parameters in a Cahn–Hilliard type diffuse interface model in an optimised way using model order reduction (MOR) based on proper orthogonal decomposition (POD). Based on snapshots derived from finite element simulations for the full‐order model (FOM), we use POD for dimension reduction and solve the parameter estimation for the reduced‐order model (ROM). Neuroimaging data are used to define the highly inhomogeneous diffusion tensors as well as to define a target functional in a patient‐specific manner. The ROM heavily relies on the discrete empirical interpolation method, which has to be appropriately adapted in order to deal with the highly nonlinear and degenerate parabolic partial differential equations. A feature of the approach is that we iterate between full order solvers with new parameters to compute a POD basis function and sensitivity‐based parameter estimation for the ROM problems. The algorithm is applied using neuroimaging data for two clinical test cases, and we can demonstrate that the reduced‐order approach drastically decreases the computational effort. [ABSTRACT FROM AUTHOR]

Fire blight is the most damaging bacterial disease in apple production worldwide. Cankers and symptomless infected shoots are known as sites for the overwintering of Erwinia amylovora, subsequently providing primary inoculum for infection in the spring. In the present work, further potential sources of inoculum were investigated. Real-time PCR assays covering a 3-year-period classified 19·9% of samples taken from fruit mummies as positive. Bacterial abundance in fruit mummies during autumn, winter and spring was up to 109 cells per gram of tissue and correlated well with later infection rates of blossoms. Blossoms of non-host plants growing close to infected trees were also shown to be colonized by E. amylovora and to enable epiphytic survival and propagation of bacteria. The results indicate a potential role of fruit mummies and buds in overwintering and as a source of primary inoculum for dissemination of the pathogen early in the growing season. Non-host blossoms may also serve as an inoculum source in the build-up of the pathogen population. Both aspects may contribute significantly to the epidemiology of E. amylovora. The significance of infected rootstocks as an inoculum source is also discussed. Fruit mummies might be used to determine pathogen pressure in an orchard before the beginning of the blooming period. [ABSTRACT FROM AUTHOR]

Genome-wide mapping approaches in diverse populations are powerful tools to unravel the genetic architecture of complex traits. The main goals of our study were to investigate the potential and limits to unravel the genetic architecture and to identify the factors determining the accuracy of prediction of the genotypic variation of Fusarium head blight (FHB) resistance in wheat (Triticum aestivum L.) based on data collected with a diverse panel of 372 European varieties. The wheat lines were phenotyped in multi-location field trials for FHB resistance and genotyped with 782 simple sequence repeat (SSR) markers, and 9k and 90k single-nucleotide polymorphism (SNP) arrays. We applied genome-wide association mapping in combination with fivefold crossvalidations and observed surprisingly high accuracies of prediction for marker-assisted selection based on the detected quantitative trait loci (QTLs). Using a random sample of markers not selected for marker-trait associations revealed only a slight decrease in prediction accuracy compared with marker-based selection exploiting the QTL information. The same picture was confirmed in a simulation study, suggesting that relatedness is a main driver of the accuracy of prediction in marker-assisted selection of FHB resistance. When the accuracy of prediction of three genomic selection models was contrasted for the three marker data sets, no significant differences in accuracies among marker platforms and genomic selection models were observed. Marker density impacted the accuracy of prediction only marginally. Consequently, genomic selection of FHB resistance can be implemented most cost-efficiently based on low- to medium-density SNP arrays. [ABSTRACT FROM AUTHOR]