Limits on Sizes of Fundamental Particles and on Gravitational Mass of a Scalar

We review the experimental limits on mass of excited fundamental particles and contact interaction energy scale parameters $\Lambda$ for QCD, QED and electroweak reactions. In particular we have focused on the QED reaction \EEGG at the energies from 91GeV{} to 202GeV{} using the differential cross-sections measured by the L3 Collaboration from 1991 to 1999. A global fit leads to lower limits at $95$ CL on $\Lambda > 1687$ GeV, which restricts the characteristic QED size of the interaction region to $R_{e} < 1.17 \times 10^{-17}$ cm. All the interaction regions are found to be smaller than the Compton wavelength of the fundamental particles. This constraint is used to estimate a lower limit on the size of a fundamental particle related to gravitational interaction, applying the model of self-gravitating particle-like structure with the de Sitter vacuum core. It gives $r_{\tau} \geq 2.3 \times {10^{-17}}$ cm and $r_{e} \geq 1.5 \times 10^{-18}$ cm, if leptons get masses at the electroweak scale, and $r_{\tau} \geq 3.3 \times {10^{-27}}$ cm, $r_{e} \geq 4.9 \times 10^{-26}$ cm, as the most stringent limits required by causality arguments. This sets also an upper limit on the gravitational mass of a scalar $m_{scalar} \leq{154}$ GeV{} at the electroweak scale and (m_{scalar} \leq \sqrt{3/8} m_{Pl}) as the most stringent limit.Comment: 8 pages, 2 pictures; Minor changes have been mad

A Bayesian Approach to Discovering Truth from Conflicting Sources for Data Integration

In practical data integration systems, it is common for the data sources being integrated to provide conflicting information about the same entity. Consequently, a major challenge for data integration is to derive the most complete and accurate integrated records from diverse and sometimes conflicting sources. We term this challenge the truth finding problem. We observe that some sources are generally more reliable than others, and therefore a good model of source quality is the key to solving the truth finding problem. In this work, we propose a probabilistic graphical model that can automatically infer true records and source quality without any supervision. In contrast to previous methods, our principled approach leverages a generative process of two types of errors (false positive and false negative) by modeling two different aspects of source quality. In so doing, ours is also the first approach designed to merge multi-valued attribute types. Our method is scalable, due to an efficient sampling-based inference algorithm that needs very few iterations in practice and enjoys linear time complexity, with an even faster incremental variant. Experiments on two real world datasets show that our new method outperforms existing state-of-the-art approaches to the truth finding problem.Comment: VLDB201

Fault diagnosis method for energy storage mechanism of high voltage circuit breaker based on CNN characteristic matrix constructed by sound-vibration signal

Aiming at the problem that some traditional high voltage circuit breaker fault diagnosis methods were over-dependent on subjective experience, the accuracy was not very high and the generalization ability was poor, a fault diagnosis method for energy storage mechanism of high voltage circuit breaker, which based on Convolutional Neural Network (CNN) characteristic matrix constructed by sound-vibration signal ,was proposed. In this paper, firstly, the morphological filtering was used for background noise cancellation of sound signal, and the time scale alignment method based on kurtosis and envelope similarity were proposed to ensure the synchronism of the sound-vibration signal. Secondly, the Pearson correlation coefficient was used to construct two-dimensional image characteristic matrix for the expanded sound-vibration signal. Finally, the characteristic matrix was trained by utilizing CNN. Local Response Normalization (LRN) and core function decorrelation were utilized to improve the structure of CNN model, which reduced the bad impact of large data fluctuation of energy storage process on the diagnostic accuracy of circuit breaker energy storage mechanism. Compared with the traditional method, the proposed method has obvious advantages, whose total accurate rate up to 98.2 % and generalization performance is excellent

A computer simulation of stress transfer in carbon nanotube/polymer nanocomposites

The reinforcing efficiency or stress transfer of carbon nanotubes (CNT) on polymers in polymer/CNT composites mainly is controlled by the polymer-CNT interface. Enhancement of polymer-CNT interactions and interfacial crystallisation is as an important way for improvement of the reinforcement experimentally. However, it is not clear about the crystallisation and orientation of polymer chains on the CNT surface, and how the interfacial crystallisation layer affects the failure of the composite. In this work, poly(vinyl alcohol)/CNT nanocomposites was selected as an example and based on the molecular dynamics simulation, the crystallisation process, failure behaviour and stress transfer in poly(vinyl alcohol)/CNT nanocomposites were analysed. The crystallisation temperature of the polymer chains on the CNT surface is slightly higher than the bulk crystallisation temperature. CNT induced crystallisation can be divided into three stages: chain folding, orientating and growing on the CNT surface. A slower crack growth was observed in the interfacial crystallised polymer/CNT systems, compare to relative amorphous systems. The effect of the interfacial crystalline layer on stress transfer is similar as enhanced polymer-CNT interaction systems. The change of the polymer-CNT surficial energy to strain has been used to analyse the interfacial failure and the stress transfer