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Classical iterative methods for tomographic reconstruction include the class of Algebraic Reconstruction Techniques (ART). Convergence of these stationary linear iterative methods is however notably slow. In this paper we propose the use of Krylov solvers for tomographic linear inversion problems. These advanced iterative methods feature fast convergence at the expense of a higher computational cost per iteration, causing them to be generally uncompetitive without the inclusion of a suitable preconditioner. Combining elements from standard multigrid (MG) solvers and the theory of wavelets, a novel wavelet-based multi-level (WMG) preconditioner is introduced, which is shown to significantly speed-up Krylov convergence. The performance of the WMG-preconditioned Krylov method is analyzed through a spectral analysis, and the approach is compared to existing methods like the classical Simultaneous Iterative Reconstruction Technique (SIRT) and unpreconditioned Krylov methods on a 2D tomographic benchmark problem. Numerical experiments are promising, showing the method to be competitive with the classical Algebraic Reconstruction Techniques in terms of convergence speed and overall performance (CPU time) as well as precision of the reconstruction.

Classical iterative methods for tomographic reconstruction include the class of Algebraic Reconstruction Techniques (ART). Convergence of these stationary linear iterative methods is however notably slow. In this paper we propose the use of Krylov solvers for tomographic linear inversion problems. These advanced iterative methods feature fast convergence at the expense of a higher computational cost per iteration, causing them to be generally uncompetitive without the inclusion of a suitable preconditioner. Combining elements from standard multigrid (MG) solvers and the theory of wavelets, a novel wavelet-based multi-level (WMG) preconditioner is introduced, which is shown to significantly speed-up Krylov convergence. The performance of the WMG-preconditioned Krylov method is analyzed through a spectral analysis, and the approach is compared to existing methods like the classical Simultaneous Iterative Reconstruction Technique (SIRT) and unpreconditioned Krylov methods on a 2D tomographic benchmark problem. Numerical experiments are promising, showing the method to be competitive with the classical Algebraic Reconstruction Techniques in terms of convergence speed and overall performance (CPU time) as well as precision of the reconstruction.

Increasing evidence supports the existence of ordered nanodomains (or rafts) in cholesterol rich cell membranes. Here authors present molecular dynamics simulations and EPR experiments to monitor permeation of oxygen and water through membranes in the liquid ordered and liquid disordered phases.

A summary of the technical advances that are incorporated in the fourth major release of the Q-Chem quantum chemistry program is provided, covering approximately the last seven years. These include developments in density functional theory methods and algorithms, nuclear magnetic resonance (NMR) property evaluation, coupled cluster and perturbation theories, methods for electronically excited and open-shell species, tools for treating extended environments, algorithms for walking on potential surfaces, analysis tools, energy and electron transfer modelling, parallel computing capabilities, and graphical user interfaces. In addition, a selection of example case studies that illustrate these capabilities is given. These include extensive benchmarks of the comparative accuracy of modern density functionals for bonded and non-bonded interactions, tests of attenuated second order Møller-Plesset (MP2) methods for intermolecular interactions, a variety of parallel performance benchmarks, and tests of the accuracy of implicit solvation models. Some specific chemical examples include calculations on the strongly correlated Cr2 dimer, exploring zeolite-catalysed ethane dehydrogenation, energy decomposition analysis of a charged ter-molecular complex arising from glycerol photoionisation, and natural transition orbitals for a Frenkel exciton state in a nine-unit model of a self-assembling nanotube.

Study region: The Nyando River basin in western Kenya. It is part of the Lake Victoria basin. Study focus: As a first step towards numerical modeling, complementary datasets from different periods have been used to develop a conceptual hydrogeological model for this geologically complex basin. The study focused on characterizing the physical and process structures. Data from geological reports and geo-electrical survey reports were used to establish aquifer extents. Timeline comparison and interpolation of groundwater levels and hydrochemical data analysis were used to define groundwater flow characteristics. New hydrological insights for the region: A highland and a Kano Plains groundwater system exist and are separated by the faults at the Nandi and Kendu escarpments. The groundwater divide between Nyando and Nyamasaria Rivers flowing through the Kano Plains is indistinct, suggesting that their lowland groundwater system is the same. Kano Plains has a shallow and deep groundwater system, both occurring in highly heterogeneous and aquifers; the bedrock here occurs at >250 m below ground level. Generally, the groundwater flow is topography driven towards Lake Victoria, but it is not clear whether it discharges here. Rainfall has minimal effect on long-term groundwater variability. Because groundwater levels do not change significantly over time, steady-state groundwater flow occurs in the plains.