342 research outputs found
Granular Pressure and the Thickness of a Layer Jamming on a Rough Incline
Dense granular media have a compaction between the random loose and random
close packings. For these dense media the concept of a granular pressure
depending on compaction is not unanimously accepted because they are often in a
"frozen" state which prevents them to explore all their possible microstates, a
necessary condition for defining a pressure and a compressibility
unambiguously. While periodic tapping or cyclic fluidization have already being
used for that exploration, we here suggest that a succession of flowing states
with velocities slowly decreasing down to zero can also be used for that
purpose. And we propose to deduce the pressure in \emph{dense and flowing}
granular media from experiments measuring the thickness of the granular layer
that remains on a rough incline just after the flow has stopped.Comment: 10 pages, 2 figure
Stationary shear flows of dense granular materials : a tentative continuum modelling
We propose a simple continuum model to interpret the shearing motion of
dense, dry and cohesion-less granular media. Compressibility, dilatancy and
Coulomb-like friction are the three basic ingredients. The granular stress is
split into a rate-dependent part representing the rebound-less impacts between
grains and a rate-independent part associated with long-lived contacts. Because
we consider stationary flows only, the grain compaction and the grain velocity
are the two main variables. The predicted velocity and compaction profiles are
in apparent agreement with the experimental or numerical results concerning
free-surface shear flows as well as confined shear flow
Fast and Accurate Camera Covariance Computation for Large 3D Reconstruction
Estimating uncertainty of camera parameters computed in Structure from Motion
(SfM) is an important tool for evaluating the quality of the reconstruction and
guiding the reconstruction process. Yet, the quality of the estimated
parameters of large reconstructions has been rarely evaluated due to the
computational challenges. We present a new algorithm which employs the sparsity
of the uncertainty propagation and speeds the computation up about ten times
\wrt previous approaches. Our computation is accurate and does not use any
approximations. We can compute uncertainties of thousands of cameras in tens of
seconds on a standard PC. We also demonstrate that our approach can be
effectively used for reconstructions of any size by applying it to smaller
sub-reconstructions.Comment: ECCV 201
Deep Modeling of Growth Trajectories for Longitudinal Prediction of Missing Infant Cortical Surfaces
Charting cortical growth trajectories is of paramount importance for
understanding brain development. However, such analysis necessitates the
collection of longitudinal data, which can be challenging due to subject
dropouts and failed scans. In this paper, we will introduce a method for
longitudinal prediction of cortical surfaces using a spatial graph
convolutional neural network (GCNN), which extends conventional CNNs from
Euclidean to curved manifolds. The proposed method is designed to model the
cortical growth trajectories and jointly predict inner and outer cortical
surfaces at multiple time points. Adopting a binary flag in loss calculation to
deal with missing data, we fully utilize all available cortical surfaces for
training our deep learning model, without requiring a complete collection of
longitudinal data. Predicting the surfaces directly allows cortical attributes
such as cortical thickness, curvature, and convexity to be computed for
subsequent analysis. We will demonstrate with experimental results that our
method is capable of capturing the nonlinearity of spatiotemporal cortical
growth patterns and can predict cortical surfaces with improved accuracy.Comment: Accepted as oral presentation at IPMI 201
Macro deformation and micro structure of 3D granular assemblies subjected to rotation of principal stress axes
This paper presents a numerical investigation on the behavior of three dimensional granular materials during continuous rotation of principal stress axes using the discrete element method. A dense specimen has been prepared as a representative element using the deposition method and subjected to stress rotation at different deviatoric stress levels. Significant plastic deformation has been observed despite that the principal stresses are kept constant. This contradicts the classical plasticity theory, but is in agreement with previous laboratory observations on sand and glass beads. Typical deformation characteristics, including volume contraction, deformation non-coaxiality, have been successfully reproduced. After a larger number of rotational cycles, the sample approaches the ultimate state with constant void ratio and follows a periodic strain path. The internal structure anisotropy has been quantified in terms of the contact-based fabric tensor. Rotation of principal stress axes densifies the packing, and leads to the increase in coordination numbers. A cyclic rotation in material anisotropy has been observed. The larger the stress ratio, the structure becomes more anisotropic. A larger fabric trajectory suggests more significant structure re-organization when rotating and explains the occurrence of more significant strain rate. The trajectory of the contact-normal based fabric is not centered in the origin, due to the anisotropy in particle orientation generated during sample generation which is persistent throughout the shearing process. The sample sheared at a lower intermediate principal stress ratio (b=0.0) (b=0.0) has been observed to approach a smaller strain trajectory as compared to the case b=0.5 b=0.5 , consistent with a smaller fabric trajectory and less significant structural re-organisation. It also experiences less volume contraction with the out-of plane strain component being dilative
Developmental toxicity and brain aromatase induction by high genistein concentrations in zebrafish embryos
Genistein is a phytoestrogen found at a high level in soybeans. In vitro and in vivo studies showed that high concentrations of genistein caused toxic effects. This study was designed to test the feasibility of zebrafish embryos for evaluating developmental toxicity and estrogenic potential of high genistein concentrations. The zebrafish embryos at 24 h post-fertilization were exposed to genistein (1 × 10−4 M, 0.5 × 10−4 M, 0.25 × 10−4 M) or vehicle (ethanol, 0.1%) for 60 h. Genistein-treated embryos showed decreased heart rates, retarded hatching times, decreased body length, and increased mortality in a dose-dependent manner. After 0.25 × 10−4 M genistein treatment, malformations of survived embryos such as pericardial edema, yolk sac edema, and spinal kyphosis were also observed. TUNEL assay results showed apoptotic DNA fragments in brain. This study also confirmed the estrogenic potential of genistein by EGFP expression in the brain of the mosaic reporter zebrafish embryos. This study first demonstrated that high concentrations of genistein caused a teratogenic effect on zebrafish embryos and confirmed the estrogenic potential of genistein in mosaic reporter zebrafish embryos
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