Broad-line and Multi-wave Band Emission from Blazars

We study the correlations of the flux of the broad-line emission ($F_{BLR}$) with the X-ray emission flux, optical emission flux at 5500 \AA and radio emission flux at 5 GHz, respectively, for a large sample of 50 Blazars (39 flat-spectrum radio quasars (FSRQs) and 11 BL Lac objects). Our main results are as follows. There are very strong correlations between $F_{BLR}$ and $F_{X}$ and between $L_{BLR}$ and $L_{X}$ in both states for 39 FSRQs and the slopes of the linear regression equations are almost equal to 1. There are weak correlations between $F_{BLR}$ and $F_{X}$ and between $L_{BLR}$ and $L_{X}$ for 11 BL Lac objects in both states, and the slopes of the linear regression equations are close to 1. There are significant correlations between $F_{BLR}$ and $F_{X}$ and between $L_{BLR}$ and $L_{X}$ for 50 blazars in both states, the slopes of both the linear regression equations are also close to 1. These results support a close link between relativistic jets and accretion on to the central Kerr black hole. On the other hand, we find that BL Lac objects have low accretion efficiency $\eta$, whereas FSRQs have high accretion efficiency $\eta$. The unified model of FSRQs and BL Lac objects is also discussed.Comment: 15 pages, 8 figure

Effect of blood's velocity on blood resistivity

Blood resistivity is an important quantity whose value influences the results of various methods used in the study of heart and circulation. In this paper, the relationship between blood resistivity and velocity of blood flow was evaluated and analyzed based upon a probe using six-ring electrodes and a circulatory model. The experimental results indicated that the change in blood resistivity was only ±1.1% when the velocity of blood flow changed from 2.83 to 40 cm/s and it rose to 23% when the velocity was lower than 2.83 cm/s

Probabilistic Monte-Carlo method for modelling and prediction of electronics component life

Power electronics are widely used in electric vehicles, railway locomotive and new generation aircrafts. Reliability of these components directly affect the reliability and performance of these vehicular platforms. In recent years, several research work about reliability, failure mode and aging analysis have been extensively carried out. There is a need for an efficient algorithm able to predict the life of power electronics component. In this paper, a probabilistic Monte-Carlo framework is developed and applied to predict remaining useful life of a component. Probability distributions are used to model the component’s degradation process. The modelling parameters are learned using Maximum Likelihood Estimation. The prognostic is carried out by the mean of simulation in this paper. Monte-Carlo simulation is used to propagate multiple possible degradation paths based on the current health state of the component. The remaining useful life and confident bounds are calculated by estimating mean, median and percentile descriptive statistics of the simulated degradation paths. Results from different probabilistic models are compared and their prognostic performances are evaluated

Theoretical and experimental studies of a novel cone-jet sensor

Modeling of a novel cone-jet sensor using two-dimensional (2-D) finite element analysis was investigated for dimensional measurement. Theoretical and experimental studies demonstrated that a cone-jet sensor supplied with air can be used to accurately measure displacement, and its work range of 1.5 to 4.2 mm is some ten times greater than a simple back-pressure sensor. It is anticipated that this type of sensor will find wide applications in manufacturing industry due to its wider working range, high precision, and other features

Complete gradient-LC-ESI system on a chip for protein analysis

This paper presents the first fully integrated gradient-elution liquid chromatography-electrospray ionization (LC-ESI) system on a chip. This chip integrates a pair of high-pressure gradient pumps, a sample injection pump, a passive mixer, a packed separation column, and an ESI nozzle. We also present the successful on-chip separation of protein digests by reverse phase (RP)-LC coupled with on-line mass spectrometer (MS) analysis

GhostVLAD for set-based face recognition

The objective of this paper is to learn a compact representation of image sets for template-based face recognition. We make the following contributions: first, we propose a network architecture which aggregates and embeds the face descriptors produced by deep convolutional neural networks into a compact fixed-length representation. This compact representation requires minimal memory storage and enables efficient similarity computation. Second, we propose a novel GhostVLAD layer that includes {\em ghost clusters}, that do not contribute to the aggregation. We show that a quality weighting on the input faces emerges automatically such that informative images contribute more than those with low quality, and that the ghost clusters enhance the network's ability to deal with poor quality images. Third, we explore how input feature dimension, number of clusters and different training techniques affect the recognition performance. Given this analysis, we train a network that far exceeds the state-of-the-art on the IJB-B face recognition dataset. This is currently one of the most challenging public benchmarks, and we surpass the state-of-the-art on both the identification and verification protocols.Comment: Accepted by ACCV 201