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Factorizing the Changes in CO2 Emissions from Indian Road Passenger Transport: A Decomposition Analysis

7. References Agnolucci, P., Ekins, P., Iacopini, G., Anderson, K., Bows, A., Mander, S. and Shackley, S. (2009), “Different scenarios for achieving radical reduction in carbon emissions: A decomposition analysis”, Ecological Economics , Vol. 68 No. 6, pp. 1652–1666. Ang, B.. (2004), “Decomposition analysis for policymaking in energy”: Energy Policy , Vol. 32 No. 9, pp. 1131–1139. Ang, B.W. (2005), “The LMDI approach to decomposition analysis: a practical guide”, Energy Policy , Vol. 33 No. 7, pp. 867–871. Ang, B.W. and Choi, K

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Decomposing the Role of Different Factors in CO2 Emissions Increase in South Asia

), University of Queensland, available online at https://www.researchgate.net/profile/Mohammad_Alauddin2/publication/24119634_Environmentalising_Economic_Development_a_South_East_Asian_Perspective/links/00b4952462e237ff43000000.pdf Ang, B.. (2004), Decomposition analysis for policymaking in energy, Energy Policy, Vol. 32, no. 9, pp. 1131–1139. Ang, B. W. (2005), T he LMDI approach to decomposition analysis: a practical guide, Energy Policy, Vol. 33, no. 7, pp. 867–871. Ang, B. W., Zhang, F. Q. (2000), A survey of index decomposition analysis in energy

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Changes in the Energy Consumption in EU-27 Countries

References ANG, B. W., NA LIU. (2007). Energy decomposition analysis: IEA model versus other methods. Energy Policy. 35: pp. 1426-1432. ANG, B. W., ZHANG, F. Q. (2000). A survey of index decomposition analysis in energy and environmental studies. Energy. 25: pp. 1149-1176. BARRO, R. J., SALA-I-MARTIN, X. (1992). Convergence. Journal of Political Economy. 100, pp. 407-443. CLEVELAND, C. J., KAUFMANN, R. K., STERN, D. I. (2000). Aggregation and the role

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Singular value decomposition analysis of back projection operator of maximum likelihood expectation maximization PET image reconstruction

Abstract

Background

In emission tomography maximum likelihood expectation maximization reconstruction technique has replaced the analytical approaches in several applications. The most important drawback of this iterative method is its linear rate of convergence and the corresponding computational burden. Therefore, simplifications are usually required in the Monte Carlo simulation of the back projection step. In order to overcome these problems, a reconstruction code has been developed with graphical processing unit based Monte Carlo engine which enabled full physical modelling in the back projection.

Materials and methods

Code performance was evaluated with simulations on two geometries. One is a sophisticated scanner geometry which consists of a dodecagon with inscribed circle radius of 8.7 cm, packed on each side with an array of 39 × 81 LYSO detector pixels of 1.17 mm sided squares, similar to a Mediso nanoScan PET/CT scanner. The other, simplified geometry contains a 38,4mm long interval as a voxel space, detector pixels are assigned in two parallel sections each containing 81 crystals of a size 1.17×1.17 mm.

Results

We have demonstrated that full Monte Carlo modelling in the back projection step leads to material dependent inhomogeneities in the reconstructed image. The reasons behind this apparently anomalous behaviour was analysed in the simplified system by means of singular value decomposition and explained by different speed of convergence.

Conclusions

To still take advantage of the higher noise stability of the full physical modelling, a new filtering technique is proposed for convergence acceleration. Some theoretical considerations for the practical implementation and for further development are also presented.

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Adverse Incentive Effects of the Unemployment Benefit Level in Romania

Abstract

This paper proposes an empirical analysis of the effects of unemployment benefit on unemployment in Romania. First, the existence of a long-run equilibrium relation between the two variables was checked using single-equation cointegration tests. The results showed that such a relation does not exist. Next, in order to evaluate the short-term effects of unemployment benefit on unemployment level, a VAR analysis was employed. Impulse response functions analysis showed that the number of persons registered as unemployed is expecting to rise as the value of monthly unemployment benefit is increasing. However, the variance decomposition analysis pointed out that only a small part (under 5%) of unemployment short-term dynamics could be explained by potential shocks in the unemployment benefit level.

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Component Resolved IR Bleaching Study of the Blue LM-OSL Signal of Various Quartz Samples

Component Resolved IR Bleaching Study of the Blue LM-OSL Signal of Various Quartz Samples

The present work provides an initial component resolved analysis concerning the effect of infra-red (IR) exposure at elevated temperatures on the blue LM-OSL signal of quartz (stimulated at 470 nm). The study was performed on a total of seven quartz samples, among which five originated from Turkey, one from Greece and one synthetic quartz sample. For these quartz samples, the presence of 6 or even 7 independent LM-OSL components was previously reported, after the application of a computerized decomposition analysis. IR bleaching of each one of these components is studied and compared to the respective signal reduction due to the same thermal treatment solely. It is clearly demonstrated that IR stimulation at temperatures above 50°C does not deplete only the fast component in most sedimentary quartz samples studied. Net depletion of fast and medium components resulting from IR exposure is sample-dependent and occurs faster as the stimulation temperature increases. Weak IR bleaching of slow components is also reported in some cases, being more effective for stimulation temperatures up to 100°C. No depletion of either the medium or the slow components was detected for stimulation temperatures above 150°C. Finally, IR does not stimulate any of the LM-OSL components in the case of the synthetic quartz sample.

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Examining multi-level effects on corporate social responsibility and irresponsibility

Abstract

What influences firms to engage in socially responsible (irresponsible) activities? Corporate social responsibility (CSR), the efforts of firms to create a positive and desirable impact on society, and corporate social irresponsibility (CSI), contrary actions of unethical behavior that negatively influence society, have become an important focus of discussion for both corporations and scholars. Despite this interest, our understanding of organizations’ socially responsible (irresponsible) actions and their antecedents is still developing. A dearth of knowledge about the multi-level nature of the drivers of CSR and CSI continues to exist. Utilizing a longitudinal sample composed of 899 firms in 66 industries, we follow a prominent model to empirically examine industry-, firm-, and individual-level effects on CSR and CSI. Employing variance decomposition analysis, our results confirm that all three levels of investigation do indeed influence CSR and CSI. More substantively, our analysis estimates the magnitude of the effects attributable to each of the three levels for both CSR and CSI. We also compare multi-level influences on two separate CSR strategies, those targeting primary stakeholders (strategic CSR) and those targeting secondary stakeholders (social CSR). We find greater industry- and firmlevel effects on social CSR, and higher individual-level effects on strategic CSR. Our results build on the conceptual work of previous authors by providing empirical analyses to confirm multilevel influences on CSR and extending prior multi-level theory to the concept of CSI. Further, we add to the emerging literature regarding stakeholder demands by examining the various influences on CSR strategies targeting different stakeholder groups.

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Regional Income Inequalities In Poland And Italy / Rozkład Nierówności Według Regionów w Polsce i We Włoszech

. (2010), Decomposition Analysis of Income Inequality in Poland by Subpopulations and Factor Components, Argumenta Oeconomica, 1(24). Krajewska A. (2010), Wzrost zróżnicowania dochodów w Polsce. Przyczyny i konsekwencje, Gospodarka Narodowa 7-8. Li, H., Squire, L. and Zou H., (1998), Explaining International and Intertemporal Variations in Income Inequality, Economic Journal108. Sztaudynger J., Kumor P., (2007), The Optimal Inequality of Earnings- The Econometric Analysis, Comparative Economic Research 1

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Some New Information Inequalities Involving f-Divergences

Decomposition Analysis. Amsterdam, North-Holland, 1972.

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The Impact of Female Labour Force Participation on Household Income Inequality in Switzerland

References Blossfeld, Hans-Peter, and Sandra Buchholz. 2009. Increasing Resource Inequality among Families in Modern Societies: The Mechanisms of Growing Educational Homogamy, Changes in the Division of Work in the Family and the Decline of the Male Breadwinner Model. Journal of Comparative Family Studies 40(4): 603-616. Breen, Richard, and Leire Salazar. 2010. Has Increased Women’s Educational Attainment Led to Greater Earnings Inequality in the United Kingdom? A Multivariate Decomposition Analysis. European Sociological

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