Compilation and analysis of integrated regional input-output tables for NUTS 2 regions in Ireland

In 1966, Roy Geary, Director of the ESRI, noted “the absence of any kind of import and export statistics for regions is a grave lacuna” and further noted that if regional analyses were to be developed then regional Input-Output Tables must be put on the “regular statistical assembly line”. Forty-five years later, the lacuna lamented by Geary still exists and remains the most significant challenge to the construction of regional Input-Output Tables in Ireland. The continued paucity of sufficient regional data to compile effective regional Supply and Use and Input-Output Tables has retarded the capacity to construct sound regional economic models and provide a robust evidence base with which to formulate and assess regional policy. This study makes a first step towards addressing this gap by presenting the first set of fully integrated, symmetric, Supply and Use and domestic Input-Output Tables compiled for the NUTS 2 regions in Ireland: The Border, Midland and Western region and the Southern & Eastern region. These tables are general purpose in nature and are consistent fully with the official national Supply & Use and Input-Output Tables, and the regional accounts. The tables are constructed using a survey-based or bottom-up approach rather than employing modelling techniques, yielding more robust and credible tables. These tables are used to present a descriptive statistical analysis of the two administrative NUTS 2 regions in Ireland, drawing particular attention to the underlying structural differences of regional trade balances and composition of Gross Value Added in those regions. By deriving regional employment multipliers, Domestic Demand Employment matrices are constructed to quantify and illustrate the supply chain impact on employment. In the final part of the study, the predictive capability of the Input-Output framework is tested over two time periods. For both periods, the static Leontief production function assumptions are relaxed to allow for labour productivity. Comparative results from this experiment are presented.

Cross-sectoral differences in the drivers of innovation: evidence from the Irish Community Innovation Survey

Purpose: The purpose of this paper is to analyse differences in the drivers of firm innovation performance across sectors. The literature often makes the assumption that firms in different sectors differ in their propensity to innovate but not in the drivers of innovation. The authors empirically assess whether this assumption is accurate through a series of econometric estimations and tests. Design/methodology/approach: The data used are derived from the Irish Community Innovation Survey 2004-2006. A series of multivariate probit models are estimated and the resulting coefficients are tested for parameter stability across sectors using likelihood ratio tests. Findings: The results indicate that there is a strong degree of heterogeneity in the drivers of innovation across sectors. The determinants of process, organisational, new to firm and new to market innovation varies across sectors suggesting that the pooling of sectors in an innovation production function may lead to biased inferences. Research limitations/implications: The implications of the results are that innovation policies targeted at stimulating innovation need to be tailored to particular industries. One size fits all policies would seem inappropriate given the large degree of heterogeneity observed across the drivers of innovation in different sectors. Originality/value: The value of this paper is that it provides an empirical test as to whether it is suitable to group sectoral data when estimating innovation production functions. Most papers simply include sectoral dummies, implying that only the propensity to innovate differs across sectors and that the slope of the coefficient estimates are in fact consistent across sectors.

Comparison of deterministic, stochastic and fuzzy logic uncertainty modelling for capacity extension projects of DI/WFI pharmaceutical plant utilities with variable/dynamic demand

The last 30 years have seen Fuzzy Logic (FL) emerging as a method either complementing or challenging stochastic methods as the traditional method of modelling uncertainty. But the circumstances under which FL or stochastic methods should be used are shrouded in disagreement, because the areas of application of statistical and FL methods are overlapping with differences in opinion as to when which method should be used. Lacking are practically relevant case studies comparing these two methods. This work compares stochastic and FL methods for the assessment of spare capacity on the example of pharmaceutical high purity water (HPW) utility systems. The goal of this study was to find the most appropriate method modelling uncertainty in industrial scale HPW systems. The results provide evidence which suggests that stochastic methods are superior to the methods of FL in simulating uncertainty in chemical plant utilities including HPW systems in typical cases whereby extreme events, for example peaks in demand, or day-to-day variation rather than average values are of interest. The average production output or other statistical measures may, for instance, be of interest in the assessment of workshops. Furthermore the results indicate that the stochastic model should be used only if found necessary by a deterministic simulation. Consequently, this thesis concludes that either deterministic or stochastic methods should be used to simulate uncertainty in chemical plant utility systems and by extension some process system because extreme events or the modelling of day-to-day variation are important in capacity extension projects. Other reasons supporting the suggestion that stochastic HPW models are preferred to FL HPW models include: 1. The computer code for stochastic models is typically less complex than a FL models, thus reducing code maintenance and validation issues. 2. In many respects FL models are similar to deterministic models. Thus the need for a FL model over a deterministic model is questionable in the case of industrial scale HPW systems as presented here (as well as other similar systems) since the latter requires simpler models. 3. A FL model may be difficult to "sell" to an end-user as its results represent "approximate reasoning" a definition of which is, however, lacking. 4. Stochastic models may be applied with some relatively minor modifications on other systems, whereas FL models may not. For instance, the stochastic HPW system could be used to model municipal drinking water systems, whereas the FL HPW model should or could not be used on such systems. This is because the FL and stochastic model philosophies of a HPW system are fundamentally different. The stochastic model sees schedule and volume uncertainties as random phenomena described by statistical distributions based on either estimated or historical data. The FL model, on the other hand, simulates schedule uncertainties based on estimated operator behaviour e.g. tiredness of the operators and their working schedule. But in a municipal drinking water distribution system the notion of "operator" breaks down. 5. Stochastic methods can account for uncertainties that are difficult to model with FL. The FL HPW system model does not account for dispensed volume uncertainty, as there appears to be no reasonable method to account for it with FL whereas the stochastic model includes volume uncertainty.

Differential and numerical models of hysteretic systems with stochastic and deterministic inputs

Many deterministic models with hysteresis have been developed in the areas of economics, finance, terrestrial hydrology and biology. These models lack any stochastic element which can often have a strong effect in these areas. In this work stochastically driven closed loop systems with hysteresis type memory are studied. This type of system is presented as a possible stochastic counterpart to deterministic models in the areas of economics, finance, terrestrial hydrology and biology. Some price dynamics models are presented as a motivation for the development of this type of model. Numerical schemes for solving this class of stochastic differential equation are developed in order to examine the prototype models presented. As a means of further testing the developed numerical schemes, numerical examination is made of the behaviour near equilibrium of coupled ordinary differential equations where the time derivative of the Preisach operator is included in one of the equations. A model of two phenotype bacteria is also presented. This model is examined to explore memory effects and related hysteresis effects in the area of biology. The memory effects found in this model are similar to that found in the non-ideal relay. This non-ideal relay type behaviour is used to model a colony of bacteria with multiple switching thresholds. This model contains a Preisach type memory with a variable Preisach weight function. Shown numerically for this multi-threshold model is a pattern formation for the distribution of the phenotypes among the available thresholds.

The impact of maternal inflammation and maternal stress in the regulation of neurodevelopment and physiological function

The mechanisms governing fetal development follow a tightly regulated pattern of progression such that interference at any one particular stage is likely to have consequences for all other stages of development in the physiological system that has been affected thereafter. These disturbances can take the form of many different events but two of the most common and widely implicated in causing detrimental effects to the developing fetus are maternal immune activation (MIA) and maternal stress. MIA has been shown to cause an increase in circulating proinflammatory cytokines in both the maternal and fetal circulation. This increase in proinflammatory mediators in the fetus is thought to occur by fetal production rather than through exchange between the maternal-fetal interface. In the case of maternal stress it is increased levels of stress related hormones such as cortisol/corticosterone which is thought to elicit the detrimental effects on fetal development. In the case of both maternal infection and stress the timing and nature of the insult generally dictates the severity and type of effects seen in affected offspring. We investigated the effect of a proinflammatory environment on neural precursor cells of which exposure resulted in a significant decrease in the normal rate of proliferation of NPCs in culture but did not have any effect on cell survival. These effects were seen to be age dependent. Using a restraint stress model we investigated the effects of prenatal stress on the development of a number of different physiological systems in the same cohort of animals. PNS animals exhibited a number of aberrant changes in cardiovascular function with altered responses to stress and hypertension, modifications in respiratory responses to hypercapnic and hypoxic challenges and discrepancies in gastrointestinal innervation. Taken together these findings suggest that both maternal infection and maternal stress are detrimental to the normal development of the fetus.

Study of insulin-like growth factor signaling in mitochondrial function

Insulin-like Growth Factor-1 (IGF-1) signalling promotes cell growth and is associated with cancer progression, including metastasis, epithelial-mesenchymal transition (EMT), and resistance to therapy. Mitochondria play an essential role in cancer cell metabolism and accumulating evidence demonstrates that dysfunctional mitochondria associated with release of mitochondrial reactive oxygen species (ROS) can influence cancer cell phenotype and invasive potential. We previously isolated a mitochondrial UTP carrier (PNC1/SLC25A33) whose expression is regulated by IGF-1, and which is essential for mitochondrial maintenance. PNC1 suppression in cancer cells results in mitochondrial dysfunction and acquisition of a profound ROS-dependent invasive (EMT) phenotype. Moreover, over-expression of PNC1 in cancer cells that exhibit an EMT phenotype is sufficient to suppress mitochondrial ROS production and reverse the invasive phenotype. This led us to investigate the IGF-1-mitochondrial signalling axis in cancer cells. We found that IGF-1 signalling supports increased mitochondrial mass and Oxphos potential through a PI3K dependant pathway. Acute inhibition of IGF-1R activity with a tyrosine kinase inhibitor results in dysfunctional mitochondria and cell death. We also observed an adaptive response to IGF-1R inhibition upon prolonged exposure to the kinase inhibitor, where increased expression of the EGF receptor can compensate for loss of mitochondrial mass through activation of PI3K/mTOR signalling. However, these cells exhibit impaired mitochondrial biogenesis and mitophagy. We conclude that the IGF-1 is required for mitochondrial maintenance and biogenesis in cancer cells, and that pharmacological inhibition of this pathway may induce mitochondrial dysfunction and may render the cells more sensitive to glycolysis-targeted drugs.