Comparative analysis of carbonization drivers in China's megacities

This study investigates the key drivers affecting emission increases in terms of population growth, economic growth, industrial transformation, and energy use in six Chinese megacities: Beijing, Shanghai, Tianjin, Chongqing, Guangzhou, and Hong Kong. The six cities represent the most-developed regions in China and they have similar per capita carbon dioxide (CO 2) emissions as many developed countries. There is an urgent need to quantify the magnitude of each factor in driving the emissions changes in those cities so that a potential bottom-up climate mitigation policy design at the city and sectoral levels can be initiated. We adopt index decomposition analysis and present the results in both additive and multiplicative approaches to reveal the absolute and relative levels of each factor in driving emission changes during 1985-2007. Among all cities, economic effect and energy intensity effect have always been the two dominant factors contributing to the changes in carbon emissions. This study reveals that there are large variations in the ways driving forces contribute to emission levels in different cities and industrial sectors. © 2012 by Yale University.

On the domain decomposition and transmission line modelling finite element method for time-domain induction motor analysis

This paper demonstrates how a finite element model which exploits domain decomposition is applied to the analysis of three-phase induction motors. It is shown that a significant gain in cpu time results when compared with standard finite element analysis. Aspects of the application of the method which are particular to induction motors are considered: the means of improving the convergence of the nonlinear finite element equations; the choice of symmetrical sub-domains; the modelling of relative movement; and the inclusion of periodic boundary conditions. © 1999 IEEE.

Morphological decomposition in Arabic ASR systems

In recent years, the use of morphological decomposition strategies for Arabic Automatic Speech Recognition (ASR) has become increasingly popular. Systems trained on morphologically decomposed data are often used in combination with standard word-based approaches, and they have been found to yield consistent performance improvements. The present article contributes to this ongoing research endeavour by exploring the use of the 'Morphological Analysis and Disambiguation for Arabic' (MADA) tools for this purpose. System integration issues concerning language modelling and dictionary construction, as well as the estimation of pronunciation probabilities, are discussed. In particular, a novel solution for morpheme-to-word conversion is presented which makes use of an N-gram Statistical Machine Translation (SMT) approach. System performance is investigated within a multi-pass adaptation/combination framework. All the systems described in this paper are evaluated on an Arabic large vocabulary speech recognition task which includes both Broadcast News and Broadcast Conversation test data. It is shown that the use of MADA-based systems, in combination with word-based systems, can reduce the Word Error Rates by up to 8.1 relative. © 2012 Elsevier Ltd. All rights reserved.

Formant estimation of speech signals using subspace-based spectral analysis

The objective of this paper is to propose a signal processing scheme that employs subspace-based spectral analysis for the purpose of formant estimation of speech signals. Specifically, the scheme is based on decimative spectral estimation that uses Eigenanalysis and SVD (Singular Value Decomposition). The underlying model assumes a decomposition of the processed signal into complex damped sinusoids. In the case of formant tracking, the algorithm is applied on a small amount of the autocorrelation coefficients of a speech frame. The proposed scheme is evaluated on both artificial and real speech utterances from the TIMIT database. For the first case, comparative results to standard methods are provided which indicate that the proposed methodology successfully estimates formant trajectories.

Characterization of light and heavy hydrated magnesium carbonates using thermal analysis

Upon heating, hydrated magnesium carbonates (HMCs) undergo a continuous sequence of decomposition reactions. This study aims to investigate the thermal decomposition of various commercially produced HMCs classified as light and heavy, highlight their differences, and provide an insight into their compositions in accordance with the results obtained from thermal analysis and microstructure studies. An understanding of the chemical compositions and microstructures, and a better knowledge of the reactions that take place during the decomposition of HMCs were achieved through the use of SEM, XRD, and TG/differential thermal analysis (DTA). The quantification of their CO 2 contents was provided by TG and dissolving the samples in HCl acid. Results show that variations exist within the microstructure and decomposition patterns of the two groups of HMCs, which do not exactly fit into the fixed stoichiometry of the known HMCs in the MgO-CO2-H2O system. The occurrence of an exothermic DTA peak was only observed for the heavy HMCs, which was attributed to their high CO2 contents and the relatively delayed decomposition pattern. © 2013 Akadémiai Kiadó, Budapest, Hungary.