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In diffusion tensor magnetic resonance imaging (DT-MRI), limitations concerning complex fiber architecture (when an image voxel contains fiber populations with more than one dominant orientation) are well-known. Fractional anisotropy (FA) values are lower in such areas because of a lower directionality of diffusion on the voxel-scale, which makes the interpretation of FA less straightforward. Moreover, the interpretation of the axial and radial diffusivities is far from trivial when there is more than one dominant fiber orientation within a voxel. In this work, using (i) theoretical considerations, (ii) simulations, and (iii) experimental data, it is demonstrated that the mean diffusivity (or the trace of the diffusion tensor) is lower in complex white matter configurations, compared with tissue where there is a single dominant fiber orientation within the voxel. We show that the magnitude of this reduction depends on various factors, including configurational and microstructural properties (e.g., the relative contributions of different fiber populations) and acquisition settings (e.g., the b-value). These results increase our understanding of the quantitative metrics obtained from DT-MRI and, in particular, the effect of the microstructural architecture on the mean diffusivity. More importantly, they reinforce the growing awareness that differences in DT-MRI metrics need to be interpreted cautiously. ► The mean diffusivity (MD) in diffusion tensor MRI is affected by crossing fibers. ► MD values are lower in complex fiber architecture than in single fiber voxels. ► This is shown using theoretical considerations, simulations and in vivo experiments. ► In vivo, mean diffusivity values decrease when fibers cross at larger angles.

The mechanical behaviour of fibre-reinforced sands is primarily governed by the three-dimensional fibre architecture within the sand matrix. In laboratory, the normal procedures for sample preparation of fibre-sand mixtures generally produce a distribution of fibre orientations with a preferential bedding orientation, generating strength anisotropy of the composite’s response under loading. While demonstrating the potential application of X-ray tomography to the analysis of fibre-reinforced soils, this paper provides for the first time a direct experimental description of the three-dimensional architecture of the fibres induced by the laboratory sample fabrication method. Miniature fibre reinforced sand samples were produced using two widely used laboratory sample fabrication techniques: the moist tamping and the moist vibration. It is shown that both laboratory fabrication methods create anisotropic fibre orientation with preferential sub-horizontal directions. The fibre orientation distribution does not seem to be affected by the concentration of fibres, at least for the fibre concentrations considered in this study and, for both fabrication methods, the fibre orientation distribution appears to be axisymmetric with respect to the vertical axis of the sample. The X-ray analysis also demonstrates the presence of an increased porosity in the fibre vicinity, which confirms the assumption of the “stolen void ratio” effect adopted in previous constitutive modelling. A fibre orientation distribution function is tested and a combined experimental and analytical method for fibre orientation determination is further validated.

In this review, we are considering an actively developing tool in neuroscience—robotic modeling. The new perspective and existing application fields, tools, and methods are discussed. We try to determine starting positions and approaches that are useful at the beginning of new research in this field. Among multiple directions of the research is robotic modeling on the level of muscles fibers and their afferents, skin surface sensors, muscles, and joints proprioceptors. Some examples of technical implementation for physical modeling are reviewed. They are software and hardware tools like event-related modeling algorithms, reduced neuron models, robotic drives constructions. We observe existing drives technologies and prospective electric motor types: switched reluctance and transverse flux motors. Next, we look at the existing examples and approaches for robotic modeling of the cerebellum and spinal cord neural networks. These examples show practical methods for the model neural network architecture and adaptation. Those methods allow the use of cortical and spinal cord reflexes for the network training and apply additional artificial blocks for data processing in other brain structures that transmit and receive data from biologically realistic models.

Quantitative knowledge of the subsurface material distribution and its effective soil hydraulic material properties is essential to predict soil water movement. Ground-penetrating radar (GPR) is a noninvasive and nondestructive geophysical measurement method that is suitable to monitor hydraulic processes. Previous studies showed that the GPR signal from a fluctuating groundwater table is sensitive to the soil water characteristic and the hydraulic conductivity function. In this work, we show that the GPR signal originating from both the subsurface architecture and the fluctuating groundwater table is suitable to estimate the position of layers within the subsurface architecture together with the associated effective soil hydraulic material properties with inversion methods. To that end, we parameterize the subsurface architecture, solve the Richards equation, convert the resulting water content to relative permittivity with the complex refractive index model (CRIM), and solve Maxwell's equations numerically. In order to analyze the GPR signal, we implemented a new heuristic algorithm that detects relevant signals in the radargram (events) and extracts the corresponding signal travel time and amplitude. This algorithm is applied to simulated as well as measured radargrams and the detected events are associated automatically. Using events instead of the full wave regularizes the inversion focussing on the relevant measurement signal. For optimization, we use a global–local approach with preconditioning. Starting from an ensemble of initial parameter sets drawn with a Latin hypercube algorithm, we sequentially couple a simulated annealing algorithm with a Levenberg–Marquardt algorithm. The method is applied to synthetic as well as measured data from the ASSESS test site. We show that the method yields reasonable estimates for the position of the layers as well as for the soil hydraulic material properties by comparing the results to references derived from ground truth data as well as from time domain reflectometry (TDR).

Despite divergent evolutionary origins, the organization of olfactory systems is remarkably similar across phyla. In both insects and mammals, sensory input from receptor cells is initially processed in synaptically dense regions of neuropil called glomeruli, where neural activity is shaped by local inhibition and centrifugal neuromodulation prior to being sent to higher-order brain areas by projection neurons. Here we review both similarities and several key differences in the neuroanatomy of the olfactory system in honey bees, mice, and humans, using a combination of literature review and new primary data. We have focused on the chemical identity and the innervation patterns of neuromodulatory inputs in the primary olfactory system. Our findings show that serotonergic fibers are similarly distributed across glomeruli in all three species. Octopaminergic/tyraminergic fibers in the honey bee also have a similar distribution, and possibly a similar function, to noradrenergic fibers in the mammalian OBs. However, preliminary evidence suggests that human OB may be relatively less organized than its counterparts in honey bee and mouse.

The fiber stretching process plays the key role in the process of fiber production and its effects is measured by the stretching ratio. The stretching ratio is determined by the relative speed of the winding roller. The stretching ratio has impact on the performance of the final fiber filament and production directly. Focused on the importance of the stretching ratio, the support vector regression (SVR) predictive model, called nonlinear auto-regressive exogenous model, for the fiber stretching rate based on existing industry data is proposed. Furthermore, the fruit fly optimization algorithm inspired by immune mechanism and cooperation functional (IFOA) is presented, and then is used to optimize the parameters in SVR. Furthermore, taking into account the high cost and accurate precision of the fiber stretching process, a time series autoregressive moving average (ARMA) model is introduced to reduce the prediction error of the IFOA–SVR model. Simulations results demonstrate that the proposed IFOA–SVR method can increase the prediction accuracy than the traditional FOA and the SVR method, and the ARMA model is essential to modify the prediction error of the IFOA–SVR model.

The mechanical behavior of continuous fiber reinforced granular columns is simulated by means of a Discrete Element Model. Spherical particles are randomly deposited simultaneously with a wire, that is deployed following different patterns inside of a flexible cylinder for triaxial compression testing. We quantify the effect of three different fiber deployment patterns on the failure envelope, represented by Mohr–Coulomb cones, and derive suggestions for improved deployment strategies.

In this paper an all-optical soliton method for calculating the Fast Fourier Transform (FFT) algorithm is presented. The method comes as an extension of the calculation methods (soliton gates) as they become possible in the cubic non-linear Schrödinger equation (3NLSE) domain, and provides a further proof of the computational abilities of the scheme. The method involves collisions entirely between first order solitons in optical fibers whose propagation evolution is described by the 3NLSE. The main building block of the arrangement is the half-adder processor. Expanding around the half-adder processor, the “butterfly” calculation process is demonstrated using first order solitons, leading eventually to the realisation of an equivalent to a full Radix-2 FFT calculation algorithm.