Genuine Correlations of Like-Sign Particles in Hadronic Z0 Decays

Correlations among hadrons with the same electric charge produced in Z0 decays are studied using the high statistics data collected from 1991 through 1995 with the OPAL detector at LEP. Normalized factorial cumulants up to fourth order are used to measure genuine particle correlations as a function of the size of phase space domains in rapidity, azimuthal angle and transverse momentum. Both all-charge and like-sign particle combinations show strong positive genuine correlations. One-dimensional cumulants initially increase rapidly with decreasing size of the phase space cells but saturate quickly. In contrast, cumulants in two- and three-dimensional domains continue to increase. The strong rise of the cumulants for all-charge multiplets is increasingly driven by that of like-sign multiplets. This points to the likely influence of Bose-Einstein correlations. Some of the recently proposed algorithms to simulate Bose-Einstein effects, implemented in the Monte Carlo model PYTHIA, are found to reproduce reasonably well the measured second- and higher-order correlations between particles with the same charge as well as those in all-charge particle multiplets.Comment: 26 pages, 6 figures, Submitted to Phys. Lett.

Information and digital literacies; a review of concepts

A detailed literature reviewing, analysing the multiple and confusing concepts around the ideas of information literacy and digital literacy at the start of the millennium. The article was well-received, and is my most highly-cited work, with over 1100 citations

A New Method for Spectral Decomposition Using a Bilinear Bayesian Approach

A frequent problem in analysis is the need to find two matrices, closely related to the underlying measurement process, which when multiplied together reproduce the matrix of data points. Such problems arise throughout science, for example, in imaging where both the calibration of the sensor and the true scene may be unknown and in localized spectroscopy where multiple components may be present in varying amounts in any spectrum. Since both matrices are unknown, such a decomposition is a bilinear problem. We report here a solution to this problem for the case in which the decomposition results in matrices with elements drawn from positive additive distributions. We demonstrate the power of the methodology on chemical shift images (CSI). The new method, Bayesian spectral decomposition (BSD), reduces the CSI data to a small number of basis spectra together with their localized amplitudes. We apply this new algorithm to a19F nonlocalized study of the catabolism of 5-fluorouracil in human liver,31P CSI studies of a human head and calf muscle, and simulations which show its strengths and limitations. In all cases, the dataset, viewed as a matrix with rows containing the individual NMR spectra, results from the multiplication of a matrix of generally nonorthogonal basis spectra (the spectral matrix) by a matrix of the amplitudes of each basis spectrum in the the individual voxels (the amplitude matrix). The results show that BSD can simultaneously determine both the basis spectra and their distribution. In principle, BSD should solve this bilinear problem for any dataset which results from multiplication of matrices representing positive additive distributions if the data overdetermine the solutions

Motives for Acquisitions and the Effects of Acquisitions on Innovation Performance: A Look at the Pharmaceutical Industry

Acquisitions have been a popular strategy for corporate growth. Their motivations and consequences have been studied extensively in the academic arena. A few empirical studies have evaluated the importance of motives for particular types of acquisitions. Consequences have focused on post-acquisition economic and innovation performance of the combined firm. Some have focused on the effects of acquisition characteristics, such as strategic and organizational fit, on the innovation performance. This thesis contributes to the current body of literature on corporate acquisitions by studying the acquisition motives of large pharmaceutical firms and by examining the effects of strategic and organizational fit of acquisitions on acquirer innovation performance. The pharmaceutical industry was chosen as a target of study because it has gone through vast consolidation recently. It is also dependent on innovation but has been seen to perform poorly in it. The possible reasons for this are examined. Based on a literature review, the acquisition motivations and their importance for each type of acquisition were identified in order to form a framework for studying the acquisition motives of pharmaceutical firms. Literature on innovation performance was used to form the hypotheses tested using regression analysis. The initial sample consisted of the 100 largest pharmaceutical firms chosen according to their 1989 sales. There are two separate parts which are analyzed in this study. One covers all the distinct acquisitions made by the sample companies between the years 1989-2003. There were a total of 236 acquisitions made by the sample firms in this period. These were used to analyze the major acquisition motivations in the pharmaceutical industry. The other part analyzes each sample company and the acquisitions that they had undergone between the years 1989-2003. These firms were used to study the effects of acquisition characteristics on acquirer technological performance. For this part of the analysis, the initial sample of 100 firms reduced down to 26 firms because some of the sample companies did not have any patent applications during the chosen years. The results on acquisition motivations show that extending product lines has been lately the most important in the pharmaceutical industry. The findings of the regression analysis reveal several things. Acquisitions do not necessarily cause a decline in innovative performance. This happens when the acquisition target is small relative to the acquirer. In this case, the acquirer can easily integrate the target to itself without disrupting its R&D activities. Also, it was shown that companies that are highly motivated in increasing their R&D scale perform better in innovation. The results have important contributions to the research on acquisitions and innovation performance. In addition, the findings have important implications for managers focused on enhancing their companies' innovative performance