2,271 research outputs found
Optical quenching and recovery of photoconductivity in single-crystal diamond
We study the photocurrent induced by pulsed-light illumination (pulse
duration is several nanoseconds) of single-crystal diamond containing nitrogen
impurities. Application of additional continuous-wave light of the same
wavelength quenches pulsed photocurrent. Characterization of the optically
quenched photocurrent and its recovery is important for the development of
diamond based electronics and sensing
Antimicrobial, mechanical and thermal studies of silver particle-loaded polyurethane.
Silver-particle-incorporated polyurethane films were evaluated for antimicrobial activity towards two different bacteria: Escherichia coli (E. coli) and Staphylococcus aureus (S. aureus). Distributed silver particles sourced from silver nitrate, silver lactate and preformed silver nanoparticles were mixed with polyurethane (PU) and variously characterized by field emission scanning electron microscopy (FESEM), fourier transform infra-red (FTIR) spectroscopy, X-ray diffraction (XRD) and contact angle measurement. Antibacterial activity against E.coli was confirmed for films loaded with 10% (w/w) AgNO3, 1% and 10% (w/w) Ag lactate and preformed Ag nanoparticles. All were active against S. aureus, but Ag nanoparticles loaded with PU had a minor effect. The apparent antibacterial performance of Ag lactate-loaded PU is better than other Ag ion-loaded films, revealed from the zone of inhibition study. The better performance of silver lactate-loaded PU was the likely result of a porous PU structure. FESEM and FTIR indicated direct interaction of silver with the PU backbone, and XRD patterns confirmed that face-centred cubic-type silver, representative of Ag metal, was present. Young's modulus, tensile strength and the hardness of silver containing PU films were not adversely affected and possibly marginally increased with silver incorporation. Dynamic mechanical analysis (DMA) indicated greater thermal stability
Testing linear hypotheses in high-dimensional regressions
For a multivariate linear model, Wilk's likelihood ratio test (LRT)
constitutes one of the cornerstone tools. However, the computation of its
quantiles under the null or the alternative requires complex analytic
approximations and more importantly, these distributional approximations are
feasible only for moderate dimension of the dependent variable, say .
On the other hand, assuming that the data dimension as well as the number
of regression variables are fixed while the sample size grows, several
asymptotic approximations are proposed in the literature for Wilk's \bLa
including the widely used chi-square approximation. In this paper, we consider
necessary modifications to Wilk's test in a high-dimensional context,
specifically assuming a high data dimension and a large sample size .
Based on recent random matrix theory, the correction we propose to Wilk's test
is asymptotically Gaussian under the null and simulations demonstrate that the
corrected LRT has very satisfactory size and power, surely in the large and
large context, but also for moderately large data dimensions like or
. As a byproduct, we give a reason explaining why the standard chi-square
approximation fails for high-dimensional data. We also introduce a new
procedure for the classical multiple sample significance test in MANOVA which
is valid for high-dimensional data.Comment: Accepted 02/2012 for publication in "Statistics". 20 pages, 2 pages
and 2 table
Microscopic mechanism for mechanical polishing of diamond (110) surfaces
Mechanically induced degradation of diamond, as occurs during polishing, is
studied using total--energy pseudopotential calculations. The strong asymmetry
in the rate of polishing between different directions on the diamond (110)
surface is explained in terms of an atomistic mechanism for nano--groove
formation. The post--polishing surface morphology and the nature of the
polishing residue predicted by this mechanism are consistent with experimental
evidence.Comment: 4 pages, 5 figure
On the Largest Singular Values of Random Matrices with Independent Cauchy Entries
We apply the method of determinants to study the distribution of the largest
singular values of large real rectangular random matrices with
independent Cauchy entries. We show that statistical properties of the
(rescaled by a factor of \frac{1}{m^2\*n^2})largest singular values agree in
the limit with the statistics of the inhomogeneous Poisson random point process
with the intensity and, therefore, are different
from the Tracy-Widom law. Among other corollaries of our method we show an
interesting connection between the mathematical expectations of the
determinants of complex rectangular standard Wishart ensemble
and real rectangular standard Wishart ensemble.Comment: We have shown in the revised version that the statistics of the
largest eigenavlues of a sample covariance random matrix with i.i.d. Cauchy
entries agree in the limit with the statistics of the inhomogeneous Poisson
random point process with the intensity $\frac{1}{\pi} x^{-3/2}.
FEATURE SELECTION APPLIED TO THE TIME-FREQUENCY REPRESENTATION OF MUSCLE NEAR-INFRARED SPECTROSCOPY (NIRS) SIGNALS: CHARACTERIZATION OF DIABETIC OXYGENATION PATTERNS
Diabetic patients might present peripheral microcirculation impairment and might benefit from physical training. Thirty-nine diabetic patients underwent the monitoring of the tibialis anterior muscle oxygenation during a series of voluntary ankle flexo-extensions by near-infrared spectroscopy (NIRS). NIRS signals were acquired before and after training protocols. Sixteen control subjects were tested with the same protocol. Time-frequency distributions of the Cohen's class were used to process the NIRS signals relative to the concentration changes of oxygenated and reduced hemoglobin. A total of 24 variables were measured for each subject and the most discriminative were selected by using four feature selection algorithms: QuickReduct, Genetic Rough-Set Attribute Reduction, Ant Rough-Set Attribute Reduction, and traditional ANOVA. Artificial neural networks were used to validate the discriminative power of the selected features. Results showed that different algorithms extracted different sets of variables, but all the combinations were discriminative. The best classification accuracy was about 70%. The oxygenation variables were selected when comparing controls to diabetic patients or diabetic patients before and after training. This preliminary study showed the importance of feature selection techniques in NIRS assessment of diabetic peripheral vascular impairmen
A New Technique for Finding Needles in Haystacks: A Geometric Approach to Distinguishing Between a New Source and Random Fluctuations
We propose a new test statistic based on a score process for determining the
statistical significance of a putative signal that may be a small perturbation
to a noisy experimental background. We derive the reference distribution for
this score test statistic; it has an elegant geometrical interpretation as well
as broad applicability. We illustrate the technique in the context of a model
problem from high-energy particle physics. Monte Carlo experimental results
confirm that the score test results in a significantly improved rate of signal
detection.Comment: 5 pages, 4 figure
EXOSOMAL MICRORNA: POTENTIAL URINARY AND PLASMA BASED BIOMARKERS FOR THE DIAGNOSIS AND PROGNOSIS OF PROSTATE CANCER
Artificial Neural Network to predict mean monthly total ozone in Arosa, Switzerland
Present study deals with the mean monthly total ozone time series over Arosa,
Switzerland. The study period is 1932-1971. First of all, the total ozone time
series has been identified as a complex system and then Artificial Neural
Networks models in the form of Multilayer Perceptron with back propagation
learning have been developed. The models are Single-hidden-layer and
Two-hidden-layer Perceptrons with sigmoid activation function. After sequential
learning with learning rate 0.9 the peak total ozone period (February-May)
concentrations of mean monthly total ozone have been predicted by the two
neural net models. After training and validation, both of the models are found
skillful. But, Two-hidden-layer Perceptron is found to be more adroit in
predicting the mean monthly total ozone concentrations over the aforesaid
period.Comment: 22 pages, 14 figure
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