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

SiO and H2O Maser Observations of Red Supergiants in Star Clusters Embedded in the Galactic Disk

We present the result of radio observations of red supergiants in the star cluster, Stephenson's #2, and candidates for red supergiants in the star clusters, Mercer et al. (2005)'s #4, #8, and #13, in the SiO and H$_2$O maser lines.The Stephenson's #2 cluster and nearby aggregation at the South-West contain more than 15 red supergiants. We detected one at the center of Stephenson's #2 and three in the south-west aggregation in the SiO maser line, and three of these 4 were also detected in the H2O maser line. The average radial velocity of the 4 detected objects is 96 km s^{-1}, giving a kinematic distance of 5.5 kpc, which locates this cluster near the base of the Scutum-Crux spiral arm. We also detected 6 SiO emitting objects associated with the other star clusters. In addition, mapping observations in the CO J=1--0 line toward these clusters revealed that an appreciable amount of molecular gas still remains around Stephenson's #2 cluster in contrast to the prototypical red-supergiant cluster, Bica et al.'s #122. It indicates that a time scale of gas expulsion differs considerably in individual clusters.Comment: high res. figures available at http://www.nro.nao.ac.jp/~lib_pub/report/data/no674.pdf. PASJ 62, No.2 (2010, April 25 issue) in pres

A detailed postprocess analysis of an argon gas puff Z-pinch plasma using SPEC2D

A postprocess analysis of a single time frame hydrodynamic profile from the PRISM two-dimensional MHD simulation of an argon gas puff Z-pinch plasma experiment on Double-Eagle generator at Physics Internationals, Co. is presented. In addition, spatially resolved emission spectra and filtered (K- and L-shell radiation) x-ray pinhole images, generated using the SPEC2D code, are examined toward the understanding of the emission characteristics of the hot spots and the formation of the Rayleigh-Taylor instability in the plasma. © 1997 American Institute of Physics.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/87687/2/283_1.pd

AXSTRAN: A non-LTE radiation transport and ionization dynamics code in axisymmetric two-dimensional geometry

A description of AXSTRAN, a fully coupled time-dependent non-LTE radiation ionization dynamics code with the nonlocal opacity effects and the radiation transport for axisymmetric 2-D geometry, is presented. A demonstration of the capability of the code is made through an examination of the radiation field and the ionization state in a finite uniform argon Z-pinch plasma. © 1997 American Institute of Physics.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/87686/2/277_1.pd

Steady-state Ab Initio Laser Theory: Generalizations and Analytic Results

We improve the steady-state ab initio laser theory (SALT) of Tureci et al. by expressing its fundamental self-consistent equation in a basis set of threshold constant flux states that contains the exact threshold lasing mode. For cavities with non-uniform index and/or non-uniform gain, the new basis set allows the steady-state lasing properties to be computed with much greater efficiency. This formulation of the SALT can be solved in the single-pole approximation, which gives the intensities and thresholds, including the effects of nonlinear hole-burning interactions to all orders, with negligible computational effort. The approximation yields a number of analytic predictions, including a "gain-clamping" transition at which strong modal interactions suppress all higher modes. We show that the single-pole approximation agrees well with exact SALT calculations, particularly for high-Q cavities. Within this range of validity, it provides an extraordinarily efficient technique for modeling realistic and complex lasers.Comment: 17 pages, 11 figure

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

A new design of nanocrystalline silicon optical devices based on 3-dimensional photonic crystal structures

We propose a new design of nanocrystalline silicon optical devices which are based on control of electromagnetic fields, electronic states, as well as the phonon dispersion of size-controlled silicon quantum dots

Renewable Energy from Living Plants to Power IoT Sensor for Remote Sensing

Renewable energy which can be used to replace traditional energy sources from fossil fuel is in dire demand to protect the earth from the further negative effect of climate change resulting from mining or drilling of fossil fuel and its related pollution. There are various renewable energy sources available, however, there is none currently that does not compete for arable land in nature or land for food production to enable the installation of the renewable energy facility. Thus, in this research, it is proposed a novel type of electrical energy which can be harvested from living plants and coexist well with nature without competing for any arable lands and at the same time generate energy for human needs. Plants generate energy from photosynthesis, respiration, and intercellular activities, and this energy, although is minute, still can be harvested as a new potential energy source to power any ultra-low power sensor for remote sensing purposes. Thus, it is presented in this paper, a characterization of the specific setup condition to harvest optimum minimum 3V from living plants and a power management circuit that can further boost the energy to an optimum level to power a wireless IoT sensor for remote sensing purposes. It turns the living plant into a plant-based cell. As there is wide vegetation in forests, jungles, plantations, and agricultural lands on earth, the combination of this energy from the plants could be a promising source of new renewable energy to mankind as this vegetation can exist for both food and energy production while it does not compete for arable land for the installation of energy sources such as what happens in fossil fuel, solar or wind energy to create greener earth