Time domain analysis of switching transient fields in high voltage substations

Switching operations of circuit breakers and disconnect switches generate transient currents propagating along the substation busbars. At the moment of switching, the busbars temporarily acts as antennae radiating transient electromagnetic fields within the substations. The radiated fields may interfere and disrupt normal operations of electronic equipment used within the substation for measurement, control and communication purposes. Hence there is the need to fully characterise the substation electromagnetic environment as early as the design stage of substation planning and operation to ensure safe operations of the electronic equipment. This paper deals with the computation of transient electromagnetic fields due to switching within a high voltage air-insulated substation (AIS) using the finite difference time domain (FDTD) metho

Immittance Spectroscopy of Smart Components and Novel Devices

AC small-signal immittance spectroscopy is employed as a viable tool to demonstrate electrical characterization, performance improvement, and quality assurance issues of smart materials-based components and novel devices. The variation in the ac response, complemented via dc measurements within a range of tolerating temperature, delineates competing phenomena occurring in the microstructures of these engineering material systems. The results are presented in a generic manner with possible explanations on the mechanisms for two selected Debye-like (nearly ideal) and non-Debye (non-ideal) low-capacitance resistors. This spectroscopic approach allows systematic development of a representative equivalent circuit, considered to be the characteristic of the devices and components, for specific applications

Noise control and utility: From regulatory network to spatial patterning

Stochasticity (or noise) at cellular and molecular levels has been observed extensively as a universal feature for living systems. However, how living systems deal with noise while performing desirable biological functions remains a major mystery. Regulatory network configurations, such as their topology and timescale, are shown to be critical in attenuating noise, and noise is also found to facilitate cell fate decision. Here we review major recent findings on noise attenuation through regulatory control, the benefit of noise via noise-induced cellular plasticity during developmental patterning, and summarize key principles underlying noise control

A close-pair binary in a distant triple supermassive black-hole system

Galaxies are believed to evolve through merging, which should lead to multiple supermassive black holes in some. There are four known triple black hole systems, with the closest pair being 2.4 kiloparsecs apart (the third component is more distant at 3 kiloparsecs), which is far from the gravitational sphere of influence of a black hole with mass $\sim$10$^9$ M$_\odot$ (about 100 parsecs). Previous searches for compact black hole systems concluded that they were rare, with the tightest binary system having a separation of 7 parsecs. Here we report observations of a triple black hole system at redshift z=0.39, with the closest pair separated by $\sim$140 parsecs. The presence of the tight pair is imprinted onto the properties of the large-scale radio jets, as a rotationally-symmetric helical modulation, which provides a useful way to search for other tight pairs without needing extremely high resolution observations. As we found this tight pair after searching only six galaxies, we conclude that tight pairs are more common than hitherto believed, which is an important observational constraint for low-frequency gravitational wave experiments.Comment: 21 pages, 6 figures. Published online by Nature on 25 June 2014. Extremely minor differences with published version exis

Analysis of pavement condition survey data for effective implementation of a network level pavement management program for Kazakhstan

Pavement roads and transportation systems are crucial assets for promoting political stability, as well as economic and sustainable growth in developing countries. However, pavement maintenance backlogs and the high capital costs of road rehabilitation require the use of pavement evaluation tools to assure the best value of the investment. This research presents a methodology for analyzing the collected pavement data for the implementation of a network level pavement management program in Kazakhstan. This methodology, which could also be suitable in other developing countries’ road networks, focuses on the survey data processing to determine cost-effective maintenance treatments for each road section. The proposed methodology aims to support a decision-making process for the application of a strategic level business planning analysis, by extracting information from the survey data

Automating Carotid Intima-Media Thickness Video Interpretation with Convolutional Neural Networks

Cardiovascular disease (CVD) is the leading cause of mortality yet largely preventable, but the key to prevention is to identify at-risk individuals before adverse events. For predicting individual CVD risk, carotid intima-media thickness (CIMT), a noninvasive ultrasound method, has proven to be valuable, offering several advantages over CT coronary artery calcium score. However, each CIMT examination includes several ultrasound videos, and interpreting each of these CIMT videos involves three operations: (1) select three end-diastolic ultrasound frames (EUF) in the video, (2) localize a region of interest (ROI) in each selected frame, and (3) trace the lumen-intima interface and the media-adventitia interface in each ROI to measure CIMT. These operations are tedious, laborious, and time consuming, a serious limitation that hinders the widespread utilization of CIMT in clinical practice. To overcome this limitation, this paper presents a new system to automate CIMT video interpretation. Our extensive experiments demonstrate that the suggested system significantly outperforms the state-of-the-art methods. The superior performance is attributable to our unified framework based on convolutional neural networks (CNNs) coupled with our informative image representation and effective post-processing of the CNN outputs, which are uniquely designed for each of the above three operations.Comment: J. Y. Shin, N. Tajbakhsh, R. T. Hurst, C. B. Kendall, and J. Liang. Automating carotid intima-media thickness video interpretation with convolutional neural networks. CVPR 2016, pp 2526-2535; N. Tajbakhsh, J. Y. Shin, R. T. Hurst, C. B. Kendall, and J. Liang. Automatic interpretation of CIMT videos using convolutional neural networks. Deep Learning for Medical Image Analysis, Academic Press, 201

Mammalian gene expression variability is explained by underlying cell state.

Gene expression variability in mammalian systems plays an important role in physiological and pathophysiological conditions. This variability can come from differential regulation related to cell state (extrinsic) and allele-specific transcriptional bursting (intrinsic). Yet, the relative contribution of these two distinct sources is unknown. Here, we exploit the qualitative difference in the patterns of covariance between these two sources to quantify their relative contributions to expression variance in mammalian cells. Using multiplexed error robust RNA fluorescent in situ hybridization (MERFISH), we measured the multivariate gene expression distribution of 150 genes related to Ca2+ signaling coupled with the dynamic Ca2+ response of live cells to ATP. We show that after controlling for cellular phenotypic states such as size, cell cycle stage, and Ca2+ response to ATP, the remaining variability is effectively at the Poisson limit for most genes. These findings demonstrate that the majority of expression variability results from cell state differences and that the contribution of transcriptional bursting is relatively minimal