Firm-level productivity growth in Northern Ireland:the impact of exporting, innovation and public financial assistance

Motivated by the historically poor productivity performance of Northern Ireland firms and the longstanding productivity gap with the UK, the aim of this thesis is to examine, through the use of firm-level data, how exporting, innovation and public financial assistance impact on firm productivity growth. These particular activities are investigated due to the continued policy focus on their link to productivity growth and the theoretical claims of a direct positive relationship. In order to undertake these analyses a newly constructed dataset is used which links together cross-sectional and longitudinal data over the 1998-2008 period from the Annual Business Survey, the Manufacturing Sales and Export Survey; the Community Innovation Survey and Invest NI Selective Financial Assistance (SFA) payment data. Econometric methodologies are employed to estimate each of the relationships with regards to productivity growth, making use in particular of Heckman selection techniques and propensity score matching to take account of critical issues of endogeneity and selection bias. The results show that more productive firms self-select into exporting but there is no resulting productivity effect from starting to export; contesting the argument for learning-by-exporting. Product innovation is also found to have no impact on productivity growth over a four year period but there is evidence of a negative process innovation impact, likely to reflect temporary learning effects. Finally SFA assistance, including the amount of the payment, is found to have no short term impact on productivity growth suggesting substantial deadweight effects and/or targeting of inefficient firms. The results provide partial evidence as to why Northern Ireland has failed to narrow the productivity gap with the rest of the UK. The analyses further highlight the need for access to comprehensive firm-level data for research purposes, not least to underpin robust evidence-based policymaking.

Spitzer power-law active galactic nucleus candidates in the chandra deep field-north

We define a sample of 62 galaxies in the Chandra Deep Field-North whose Spitzer IRAC SEDs exhibit the characteristic power-law emission expected of luminous AGNs. We study the multiwavelength properties of this sample and compare the AGNs selected in this way to those selected via other Spitzer color-color criteria. Only 55% of the power-law galaxies are detected in the X-ray catalog at exposures of >0.5 Ms, although a search for faint emission results in the detection of 85% of the power-law galaxies at the ≥2.5 σ detection level. Most of the remaining galaxies are likely to host AGNs that are heavily obscured in the X-ray. Because the power-law selection requires the AGNs to be energetically dominant in the near- and mid-infrared, the power-law galaxies comprise a significant fraction of the Spitzer-detected AGN population at high luminosities and redshifts. The high 24 μm detection fraction also points to a luminous population. The power-law galaxies comprise a subset of color-selected AGN candidates. A comparison with various mid-infrared color selection criteria demonstrates that while the color-selected samples contain a larger fraction of the X-ray-luminous AGNs, there is evidence that these selection techniques also suffer from a higher degree of contamination by star-forming galaxies in the deepest exposures. Considering only those power-law galaxies detected in the X-ray catalog, we derive an obscured fraction of 68% (2 : 1). Including all of the power-law galaxies suggests an obscured fraction of <81% (4 : 1).

On the Advancement of Probabilistic Models of Decompression Sickness

The work presented in this dissertation is focused on applying engineering methods to develop and explore probabilistic survival models for the prediction of decompression sickness in US NAVY divers. Mathematical modeling, computational model development, and numerical optimization techniques were employed to formulate and evaluate the predictive quality of models fitted to empirical data. In Chapters 1 and 2 we present general background information relevant to the development of probabilistic models applied to predicting the incidence of decompression sickness. The remainder of the dissertation introduces techniques developed in an effort to improve the predictive quality of probabilistic decompression models and to reduce the difficulty of model parameter optimization.

The first project explored seventeen variations of the hazard function using a well-perfused parallel compartment model. Models were parametrically optimized using the maximum likelihood technique. Model performance was evaluated using both classical statistical methods and model selection techniques based on information theory. Optimized model parameters were overall similar to those of previously published Results indicated that a novel hazard function definition that included both ambient pressure scaling and individually fitted compartment exponent scaling terms.

We developed ten pharmacokinetic compartmental models that included explicit delay mechanics to determine if predictive quality could be improved through the inclusion of material transfer lags. A fitted discrete delay parameter augmented the inflow to the compartment systems from the environment. Based on the observation that symptoms are often reported after risk accumulation begins for many of our models, we hypothesized that the inclusion of delays might improve correlation between the model predictions and observed data. Model selection techniques identified two models as having the best overall performance, but comparison to the best performing model without delay and model selection using our best identified no delay pharmacokinetic model both indicated that the delay mechanism was not statistically justified and did not substantially improve model predictions.

Our final investigation explored parameter bounding techniques to identify parameter regions for which statistical model failure will not occur. When a model predicts a no probability of a diver experiencing decompression sickness for an exposure that is known to produce symptoms, statistical model failure occurs. Using a metric related to the instantaneous risk, we successfully identify regions where model failure will not occur and identify the boundaries of the region using a root bounding technique. Several models are used to demonstrate the techniques, which may be employed to reduce the difficulty of model optimization for future investigations.

Optimised wheat root architecture for increased yield and yield stability in the face of a changing climate.

This project provided information, selection techniques and strategies to facilitate the development of high-yielding, stay-green wheat varieties for Australian growers through: a) Improved understanding of the relationships between seminal root traits and other root- and shoot-related traits in determining high-yielding, stay-green phenotypes. b). Molecular markers and rapid phenotypic screening methods that allow selection in breeding programs and identification of genetic regions controlling favourable traits. c). Identification of traits leading to high-yielding, stay-green phenotypes for particular target populations of environments using computer simulation studies.

An experimental study of adaptive control for evolutionary algorithms

International audience

Sociology of religion in Australia

The sociology of religion has been a moderately strong theme in Australian sociology. Most Australian sociologists of religion have been trained in Australia with a smattering of those trained in the USA, the UK or elsewhere. While Christian churches once maintained research offices including sociologists and some seminaries once included the sociology of religion in their offerings, this is no longer so. Other religious groups have not yet grown to such strength that the support of their own research sections has been possible, but several have actively funded research-including Jews, Muslims, Buddhists, and Scientology. The Christian Research Association, founded in the mid-198os, is the only independent research organization in Australia devoted to the sociology of religion. While largely funded by church organizations, it also receives government grants and has maintained its independence of religious organisations.The National Church Life Survey group, which also commenced work in the mid-198os, conducts a nation-wide survey of church attenders every five years at the time of the Australian census (e.g. Kaldor et al. 1994, 1999 ). Their time series data on Australian Catholics are excellent, being gathered according to random selection techniques. The NCLS also provides five-yearly reports on Protestants and Anglicans and other studies of congregational life in Australia. There are no systematic data sources on the Orthodox, who comprise three percent of the national population and six percent of the population in Melbourne.

Feature selection and the concept of immediate neighborhood in the context of clustering techniques

The concept of feature selection in a nonparametric unsupervised learning environment is practically undeveloped because no true measure for the effectiveness of a feature exists in such an environment. The lack of a feature selection phase preceding the clustering process seriously affects the reliability of such learning. New concepts such as significant features, level of significance of features, and immediate neighborhood are introduced which result in meeting implicitly the need for feature slection in the context of clustering techniques.

Feature selection and the concept of immediate neighborhood in the context of clustering techniques

The concept of feature selection in a nonparametric unsupervised learning environment is practically undeveloped because no true measure for the effectiveness of a feature exists in such an environment. The lack of a feature selection phase preceding the clustering process seriously affects the reliability of such learning. New concepts such as significant features, level of significance of features, and immediate neighborhood are introduced which result in meeting implicitly the need for feature slection in the context of clustering techniques.

Comparison of techniques to screen and characterize bacteria-specific hybridomas for high-quality monoclonal antibodies selection

Antibodies are are very important materials for diagnostics. A rapid and simple hybridoma screening method will help in delivering specific monoclonal antibodies. In this study, we systematically developed the first antibody array to screen for bacteria-specific monoclonal antibodies using Listeria monocytogenes as a bacteria model. The antibody array was developed to expedite the hybridoma screening process by printing hybridoma supernatants on a glass slide coated with an antigen of interest. This screening method is based on the binding ability of supernatants to the coated antigen. The bound supernatants were detected by a fluorescently labeled anti-mouse immunoglobulin. Conditions (slide types, coating, spotting, and blocking buffers) for antibody array construction were optimized. To demonstrate its usefulness, antibody array was used to screen a sample set of 96 hybridoma supernatants in comparison to ELISA. Most of the positive results identified by ELISA and antibody array methods were in agreement except for those with low signals that were undetectable by antibody array. Hybridoma supernatants were further characterized with surface plasmon resonance to obtain additional data on the characteristics of each selected clone. While the antibody array was slightly less sensitive than ELISA, a much faster and lower cost procedure to screen clones against multiple antigens has been demonstrated. (C) 2011 Elsevier Inc. All rights reserved.

Applying Data mining using Statistical Techniques for Career Selection

For years, choosing the right career by monitoring the trends and scope for different career paths have been a requirement for all youngsters all over the world. In this paper we provide a scientific, data mining based method for job absorption rate prediction and predicting the waiting time needed for 100% placement, for different engineering courses in India. This will help the students in India in a great deal in deciding the right discipline for them for a bright future. Information about passed out students are obtained from the NTMIS ( National technical manpower information system ) NODAL center in Kochi, India residing in Cochin University of science and technology