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In the present study, breaking solitary waves over a sloping seabed and breaking wave forces on a vertically mounted cylinder are simulated with the three-dimensional CFD model REEF3D. The numerical model uses the Reynolds-Averaged Navier–Stokes (RANS) equations together with the level set method (LSM) for the free surface and the $$k-\omega $$ k-ω for the turbulence. The numerical model is validated for simulating breaking solitary waves and breaking wave forces against the experimentally measured free surface profiles and vertical and horizontal velocities by Mo et al. (Ocean Eng 74:48–60, 2013) and the experimentally measured free surface elevation and breaking wave force by Chakrabarti et al. (Appl Ocean Res 19:113–140, 1997). The main purpose of the paper is to examine the effects of the breaking characteristics, the geometric properties, the relative cylinder positions and the incident wave heights on the breaking wave force characteristics. A total of 21 simulations are performed to investigate the characteristics and the geometric properties of solitary waves breaking over a slope and the associated breaking wave forces on a cylinder. First, the characteristics and geometric properties of breaking solitary waves are investigated with two-dimensional simulations. Further, the study explores the effect of the relative distance between the breaking point and the cylinder on breaking wave forces. Finally, the study examines breaking solitary wave forces for different incident waves. This also includes the analysis of breaking wave force characteristics such as the impact duration and rise time, the peak force, the average slamming coefficient and the force impulse. The results of the numerical simulations show that the relative distance between the cylinder and the breaking point plays an important role in obtaining the maximum force. In addition, the numerical model is capable of representing the most important physical flow features related to the breaking solitary waves and the interaction with the vertical slender cylinder.

Transverse relaxation time T2 and magnetization transfer ratio MTR are examples of two magnetic resonance imaging parameters that allow for a sensitive characterization of microscopic tissue properties in the brain. Classically, the mean values of these parameters are assessed in a region-of-interest and compared groupwise. In contrast to this, modern voxel-based methods test for localized changes in imaging parameters on a per-voxel basis, by registering the image to an average brain template or atlas. An intermediary method is distributional analysis, where the distribution of an imaging parameter over an anatomical region, identified from a brain atlas, is the main object of interest. This distribution captures local variation and changes in tissue properties and can ideally be described by a parametric mixture model. It can directly be compared across subjects by a distance measure and classification of subjects can be based on features extracted from these distributions of imaging parameters. This approach reduces the high dimensionality of the data and, consequently, the impact of noise and avoids the problem of collinearity. In this article the applicability of these and related methods of feature extraction and discrimination is reviewed in the context of Alzheimers disease.

Transverse relaxation time T2 and magnetization transfer ratio MTR are examples of two magnetic resonance imaging parameters that allow for a sensitive characterization of microscopic tissue properties in the brain. Classically, the mean values of these parameters are assessed in a region-of-interest and compared groupwise. In contrast to this, modern voxel-based methods test for localized changes in imaging parameters on a per-voxel basis, by registering the image to an average brain template or atlas. An intermediary method is distributional analysis, where the distribution of an imaging parameter over an anatomical region, identified from a brain atlas, is the main object of interest. This distribution captures local variation and changes in tissue properties and can ideally be described by a parametric mixture model. It can directly be compared across subjects by a distance measure and classification of subjects can be based on features extracted from these distributions of imaging parameters. This approach reduces the high dimensionality of the data and, consequently, the impact of noise and avoids the problem of collinearity. In this article the applicability of these and related methods of feature extraction and discrimination is reviewed in the context of Alzheimer’s disease.

In this paper we present an initial analysis of job arrivals in a production data-intensive Grid and investigate several traffic models for the interarrival time processes. Our analysis focuses on the heavy-tail behavior and autocorrelations, and the modeling is carried out at three different levels: Grid, Virtual Organization (VO), and region. A set of m-state Markov modulated Poisson processes (MMPP) is investigated, while Poisson processes and hyperexponential renewal processes are evaluated for comparison studies. We apply the transportation distance metric from dynamical systems theory to further characterize the differences between the data trace and the simulated time series, and estimate errors by bootstrapping. The experimental results show that MMPPs with a certain number of states are successful to a certain extent in simulating the job traffic at different levels, fitting both the interarrival time distribution and the autocorrelation function. However, MMPPs are not able to match the autocorrelations for certain VOs, in which strong deterministic semi-periodic patterns are observed. These patterns are further characterized using different representations. Future work is needed to model both deterministic and stochastic components in order to better capture the correlation structure in the series.