Space charge and charge trapping characteristics of cross-linked polyethylene subjected to ac electric stresses

This paper reports on the result of space charge evolution in cross-linked polyethylene (XLPE) planar samples of approximately 220 ?m thick. The space charge measurement technique used in this study is the PEA method. There are two phases to this experiment. In the first phase, the samples were subjected to dc 30 kVdc/mm and ac (sinusoidal) electric stress level of 30 kVpk/mm at frequencies of 1 Hz, 10 Hz and 50 Hz ac. In addition, ac space charge under 30 kVrms/mm and 60 kVpk/mm electric stress at 50 Hz was also investigated. The volts off results showed that the amount of charge trapped in XLPE sample under dc electric stress is significantly bigger than samples under ac stress even when the applied ac stresses are substantially higher. The second phase of the experiment involves studying the dc space charge evolution in samples that were tested under ac stress during the first phase of the experiment. Ac ageing causes positive charge to become more dominant over negative charge. It was also discovered that ac ageing creates deeper traps, particularly for negative charge. This paper also gave a brief overview of the data processing methods used to analyse space charge under ac electric stress

Improvement of dielectric loss of doped Ba0.5Sr0.5TiO3 thin films for tunable microwave devices

Al2O3-Ba0.5Sr0.5TiO3 (Al2O3-BST) thin films, with different Al2O3 contents, were deposited on (100) LaAlO3 substrate by pulsed laser deposition (PLD) technique. The Al2O3-BST films was demosnstrated to be a suitable systems to fabricate ferroelectric thin films with low dielectric loss and higher figure of merit for tunable microwave devices. Pure BST thin films were also fabricated for comparison purpose. The films' structure and morphology were analyzed by X-ray diffractiopn and scanning electron microscopy, respectively; nad showed that the surface roughness for the Al2O3-BST films increased with the Al2O3 content. Apart from that, the broadening in the intensity peak in XRD result indicating the grain size of the Al2O3-BST films reduced with the increasing of Al2O3 dopant. We measured the dielctric properties of Al2O3-BST films with a home-made non-destructive dual resonator method at frequency ~ 7.7 GHZ. The effect of doped Al2O3 into BST thin films significantly reduced the dielectric constant, dielectric loss and tunability compare to pure BST thin film. Our result shows the figure of merit (K), used to compare the films with varied dielectric properties, increased with the Al2O3 content. Therefore Al2O3-BST films show the potential to be exploited in tunable microwave devices.Comment: 8 pages, 4 figures, 1 table. Accepted & tentatively for Feb 15 2004 issue, Journal of Applied Physic

Inferring gene regulatory networks from gene expression data by a dynamic Bayesian network-based model

Enabled by recent advances in bioinformatics, the inference of gene regulatory networks (GRNs) from gene expression data has garnered much interest from researchers. This is due to the need of researchers to understand the dynamic behavior and uncover the vast information lay hidden within the networks. In this regard, dynamic Bayesian network (DBN) is extensively used to infer GRNs due to its ability to handle time-series microarray data and modeling feedback loops. However, the efficiency of DBN in inferring GRNs is often hampered by missing values in expression data, and excessive computation time due to the large search space whereby DBN treats all genes as potential regulators for a target gene. In this paper, we proposed a DBN-based model with missing values imputation to improve inference efficiency, and potential regulators detection which aims to lessen computation time by limiting potential regulators based on expression changes. The performance of the proposed model is assessed by using time-series expression data of yeast cell cycle. The experimental results showed reduced computation time and improved efficiency in detecting gene-gene relationships

Inferring gene regulatory networks from gene expression data by a dynamic Bayesian network-based model

Enabled by recent advances in bioinformatics, the inference of gene regulatory networks (GRNs) from gene expression data has garnered much interest from researchers. This is due to the need of researchers to understand the dynamic behavior and uncover the vast information lay hidden within the networks. In this regard, dynamic Bayesian network (DBN) is extensively used to infer GRNs due to its ability to handle time-series microarray data and modeling feedback loops. However, the efficiency of DBN in inferring GRNs is often hampered by missing values in expression data, and excessive computation time due to the large search space whereby DBN treats all genes as potential regulators for a target gene. In this paper, we proposed a DBN-based model with missing values imputation to improve inference efficiency, and potential regulators detection which aims to lessen computation time by limiting potential regulators based on expression changes. The performance of the proposed model is assessed by using time-series expression data of yeast cell cycle. The experimental results showed reduced computation time and improved efficiency in detecting gene-gene relationships

Single-Pixel Image Reconstruction Based on Block Compressive Sensing and Deep Learning

Single-pixel imaging (SPI) is a novel imaging technique whose working principle is based on the compressive sensing (CS) theory. In SPI, data is obtained through a series of compressive measurements and the corresponding image is reconstructed. Typically, the reconstruction algorithm such as basis pursuit relies on the sparsity assumption in images. However, recent advances in deep learning have found its uses in reconstructing CS images. Despite showing a promising result in simulations, it is often unclear how such an algorithm can be implemented in an actual SPI setup. In this paper, we demonstrate the use of deep learning on the reconstruction of SPI images in conjunction with block compressive sensing (BCS). We also proposed a novel reconstruction model based on convolutional neural networks that outperforms other competitive CS reconstruction algorithms. Besides, by incorporating BCS in our deep learning model, we were able to reconstruct images of any size above a certain smallest image size. In addition, we show that our model is capable of reconstructing images obtained from an SPI setup while being priorly trained on natural images, which can be vastly different from the SPI images. This opens up opportunity for the feasibility of pretrained deep learning models for CS reconstructions of images from various domain areas

Operational approach to the Uhlmann holonomy

We suggest a physical interpretation of the Uhlmann amplitude of a density operator. Given this interpretation we propose an operational approach to obtain the Uhlmann condition for parallelity. This allows us to realize parallel transport along a sequence of density operators by an iterative preparation procedure. At the final step the resulting Uhlmann holonomy can be determined via interferometric measurements.Comment: Added material, references, and journal reference

Comment on ``A new efficient method for calculating perturbative energies using functions which are not square integrable'': regularization and justification

The method recently proposed by Skala and Cizek for calculating perturbation energies in a strict sense is ambiguous because it is expressed as a ratio of two quantities which are separately divergent. Even though this ratio comes out finite and gives the correct perturbation energies, the calculational process must be regularized to be justified. We examine one possible method of regularization and show that the proposed method gives traditional quantum mechanics results.Comment: 6 pages in REVTeX, no figure

Origins of ferromagnetism in transition-metal doped Si

We present results of the magnetic, structural and chemical characterizations of Mn<sup>+</sup>-implanted Si displaying <i>n</i>-type semiconducting behavior and ferromagnetic ordering with Curie temperature,T<sub>C</sub> well above room temperature. The temperature-dependent magnetization measured by superconducting quantum device interference (SQUID) from 5 K to 800 K was characterized by three different critical temperatures (T*<sub>C</sub>~45 K, T<sub>C1</sub>~630-650 K and T<sub>C2</sub>~805-825 K). Their origins were investigated using dynamic secondary mass ion spectroscopy (SIMS) and transmission electron microscopy (TEM) techniques, including electron energy loss spectroscopy (EELS), Z-contrast STEM (scanning TEM) imaging and electron diffraction. We provided direct evidences of the presence of a small amount of Fe and Cr impurities which were unintentionally doped into the samples together with the Mn<sup>+</sup> ions, as well as the formation of Mn-rich precipitates embedded in a Mn-poor matrix. The observed T*<sub>C</sub> is attributed to the Mn<sub>4</sub>Si<sub>7</sub> precipitates identified by electron diffraction. Possible origins of and are also discussed. Our findings raise questions regarding the origin of the high ferromagnetism reported in many material systems without a careful chemical analysis