Processing-Structure-Property Relationships in Solid-State Shear Pulverization: Parametric Study of specific energy

Solid-state shear pulverization (SSSP) is a unique processing technique for mechanochemical modification of polymers, compatibilization of polymer blends, and exfoliation and dispersion of fillers in polymer nanocomposites. A systematic parametric study of the SSSP technique is conducted to elucidate the detailed mechanism of the process and establish the basis for a range of current and future operation scenarios. Using neat, single component polypropylene (PP) as the model material, we varied machine type, screw design, and feed rate to achieve a range of shear and compression applied to the material, which can be quantified through specific energy input (Ep). As a universal processing variable, Ep reflects the level of chain scission occurring in the material, which correlates well to the extent of the physical property changes of the processed PP. Additionally, we compared the operating cost estimates of SSSP and conventional twin screw extrusion to determine the practical viability of SSSP.

The Redistribution of Property Rights, Corporate Governance and Company Restructuring after Privatisation"

The project covered the main issues of privatisation, corporate governance and company restructuring after privatisation in Hungary and in the Russian Republic, together with a summary of the broader picture of company-level changes in Central and Eastern Europe, discussing the issues of micro-financial restructuring in the Czech Republic, Hungary, Poland and Slovakia. The two countries selected as the focus of research can be regarded as the two most widely differing cases of the economic transformation in Central and Eastern Europe. Hungary began its transition very early in 1989, while Russia was very late in doing so. Hungary first implemented a series of institutional and systemic reforms before stabilising its public finances, while Russia has struggled with financial stabilisation for years without great success. Company restructuring and the introduction of new forms of governance only began in Russia in the mid-1990s. Hungary opted for "traditional" western methods of privatisation and invited a large amount of foreign direct investment (FDI) while in Russia the bulk of state-owned property was privatised either by free distribution or by a strange blend of ESOP-MBO schemes. FDI in Russia remained modest because of the high risk and uncertainty surrounding economic transactions there. Hungary was a forerunner in privatising public utilities, while Russia has moved cautiously in this area. The group's studies show that the Hungarian economy is now over the "transformation recession" and its economic success is largely due to its successful privatisation and to the dominant participation of foreign investors in company take-overs and in the restructuring process. The study of Russia provides a comprehensive account of the main factors in the so-far modest results in Russian privatisation and economic transformation.

Gauss-Seidel Estimation of Generalized Linear Mixed Models with Application to Poisson Modeling of Spatially Varying Disease Rates

Generalized linear mixed models (GLMMs) provide an elegant framework for the analysis of correlated data. Due to the non-closed form of the likelihood, GLMMs are often fit by computational procedures like penalized quasi-likelihood (PQL). Special cases of these models are generalized linear models (GLMs), which are often fit using algorithms like iterative weighted least squares (IWLS). High computational costs and memory space constraints often make it difficult to apply these iterative procedures to data sets with very large number of cases. This paper proposes a computationally efficient strategy based on the Gauss-Seidel algorithm that iteratively fits sub-models of the GLMM to subsetted versions of the data. Additional gains in efficiency are achieved for Poisson models, commonly used in disease mapping problems, because of their special collapsibility property which allows data reduction through summaries. Convergence of the proposed iterative procedure is guaranteed for canonical link functions. The strategy is applied to investigate the relationship between ischemic heart disease, socioeconomic status and age/gender category in New South Wales, Australia, based on outcome data consisting of approximately 33 million records. A simulation study demonstrates the algorithm's reliability in analyzing a data set with 12 million records for a (non-collapsible) logistic regression model.

LIKELIHOOD ESTIMATION OF CONJUGACY RELATIONSHIPS IN LINEAR MODELS WITH APPLICATIONS TO HIGH-THROUGHPUT GENOMICS

In the simultaneous estimation of a large number of related quantities, multilevel models provide a formal mechanism for efficiently making use of the ensemble of information for deriving individual estimates. In this article we investigate the ability of the likelihood to identify the relationship between signal and noise in multilevel linear mixed models. Specifically, we consider the ability of the likelihood to diagnose conjugacy or independence between the signals and noises. Our work was motivated by the analysis of data from high-throughput experiments in genomics. The proposed model leads to a more flexible family. However, we further demonstrate that adequately capitalizing on the benefits of a well fitting fully-specified likelihood in the terms of gene ranking is difficult.

A SURVEY OF THE LIKELIHOOD APPROACH TO BIOEQUIVALENCE TRIALS

Bioequivalence trials are abbreviated clinical trials whereby a generic drug or new formulation is evaluated to determine if it is "equivalent" to a corresponding previously approved brand-name drug or formulation. In this manuscript, we survey the process of testing bioequivalence and advocate the likelihood paradigm for representing the resulting data as evidence. We emphasize the unique conflicts between hypothesis testing and confidence intervals in this area - which we believe are indicative of the existence of the systemic defects in the frequentist approach - that the likelihood paradigm avoids. We suggest the direct use of profile likelihoods for evaluating bioequivalence and examine the main properties of profile likelihoods and estimated likelihoods under simulation. This simulation study shows that profile likelihoods are a reasonable alternative to the (unknown) true likelihood for a range of parameters commensurate with bioequivalence research. Our study also shows that the standard methods in the current practice of bioequivalence trials offers only weak evidence from the evidential point of view.