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 $p\le 20$. On the other hand, assuming that the data dimension $p$ as well as the number $q$ of regression variables are fixed while the sample size $n$ 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 $p$ and a large sample size $n$. 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 $p$ and large $n$ context, but also for moderately large data dimensions like $p=30$ or $p=50$. 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 $m \times n$ 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 $\frac{1}{\pi} x^{-3/2}$ 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 $m \times n$ standard Wishart ensemble and real rectangular $2m \times 2n$ 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

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