Abstract: The problem of toughening in brittle materials
reinforced by fibers is complex, involving all of the mechanical
properties of fibers, matrix and the fiber/matrix interface, as well as
the geometry of the fiber. Development of new numerical methods
appropriate to toughening simulation and analysis is necessary. In
this work, we have performed simulations and analysis of toughening
in brittle matrix reinforced by randomly distributed fibers by means
of the discrete elements method. At first, we put forward a
mechanical model of toughening contributed by random fibers. Then
with a numerical program, we investigated the stress, damage and
bridging force in the composite material when a crack appeared in the
brittle matrix. From the results obtained, we conclude that: (i) fibers
of high strength and low elasticity modulus are beneficial to
toughening; (ii) fibers of relatively high elastic modulus compared to
the matrix may result in substantial matrix damage due to spalling
effect; (iii) employment of high-strength synthetic fibers is a good
option for toughening. We expect that the combination of the discrete
element method (DEM) with the finite element method (FEM) can
increase the versatility and efficiency of the software developed. The
present work can guide the design of ceramic composites of high
performance through the optimization of the parameters.
Abstract: Segmentation, filtering out of measurement errors and
identification of breakpoints are integral parts of any analysis of
microarray data for the detection of copy number variation (CNV).
Existing algorithms designed for these tasks have had some successes
in the past, but they tend to be O(N2) in either computation time or
memory requirement, or both, and the rapid advance of microarray
resolution has practically rendered such algorithms useless. Here we
propose an algorithm, SAD, that is much faster and much less thirsty
for memory – O(N) in both computation time and memory requirement
-- and offers higher accuracy. The two key ingredients of SAD are the
fundamental assumption in statistics that measurement errors are
normally distributed and the mathematical relation that the product of
two Gaussians is another Gaussian (function). We have produced a
computer program for analyzing CNV based on SAD. In addition to
being fast and small it offers two important features: quantitative
statistics for predictions and, with only two user-decided parameters,
ease of use. Its speed shows little dependence on genomic profile.
Running on an average modern computer, it completes CNV analyses
for a 262 thousand-probe array in ~1 second and a 1.8 million-probe
array in 9 seconds