Public debt and Financial development: A theoretical exploration

This paper proposes an analytical framework to examine the role of public debt in financial development, which remains largely unexplored in the existing literature. We find that in countries where the banking sector extends substantial credit to government, public debt is likely to harm financial development, with unfavourable implications for economic activity. As such, our results provide an alternative explanation for the ‘contractionary fiscal expansions’. We also show that the lower the financial depth, the greater the adverse effects of public borrowing on financial development and macroeconomic outcomes.Financial sector; credit to government; public debt.

Golden Rule of Public Finance: A Panacea?

This paper shows that adopting a golden rule does not guarantee that public investment will improve economic outcomes. Our results suggest that only when the rate of return on public capital is greater than the cost of public borrowing, expandingpublic investment is beneficial. Otherwise, both macroeconomic stability and debt sustainability are compromised. As such, we argue that policy-makers should prioritise the productivity of public investment rather than its level.Public investment, public debt, golden rule.

Regional distribution of photovoltaic deployment in the UK and its determinants: A spatial econometric approach

Photovoltaic (PV) panels offer significant potential for contributing to the UK's energy policy goals relating to decarbonisation of the energy system, security of supply and affordability. The substantive drop in the cost of panels since 2007, coupled with the introduction of the Feed-in Tariff (FiT) Scheme in 2010, has resulted in a rapid increase in installation of PV panels in the UK, from 26.5MWp in 2009 to over 5GW by the end of 2014. Yet there has been no comprehensive analysis of the determinants of PV deployment in the UK. This paper addresses this gap by employing spatial econometrics methods to a recently available data set at a fine geographical detail. Following a traditional regression analysis, a general to specific approach has been adopted where spatial variations in the relationships have been examined utilising the spatial Durbin model using the cross-sectional data relating to the UK NUTS level 3 data. Empirical results indicate that demand for electricity, population density, pollution levels, education level of households and housing types are among the factors that affect PV uptake in a region. Moreover Lagrange Multiplier test results indicate that the spatial Durbin model may be properly applied to describe the PV uptake relationship in the UK as there are significant regional spillover effects

Role of modelling on state and parameter estimation

In process industry, plants are generally operated at conditions that differ from the designed ones mainly due to disturbances. Disturbances can enter the system in form of fluctuations in feed flow, temperature and composition, or fluctuation of the utilities quality. These events cause a deterioration of the plant performance that cannot be quantified and online compensated by means of controllers unless online measurements of the quality targets (e.g. concentration, conversion, etc) are available. However the problem of online monitoring cannot be always solved in practice by means of hardware analysers because of unreliable and delayed measurements. An alternative approach is based on estimators that infer the variables of interests by means of secondary measurements and a often nonlinear model of the process. This type of realization of observers can include the online estimation of model parameters for a more accurate alignment of the model with the process behaviour. This work addresses the role of the estimation model on estimation performance. Recent studies [1, 2] pointed out that for a defined set of plant measurements the choice of the estimation model and the innovated states play a key role on the performance of the estimator regardless the algorithm employed. Even if in the cited studies some features of the estimation model (such as level of detail, computational complexity) have been taken into account, the effect on the estimation performance of model manipulations such as variables and parameters scaling [3] and transformation have not been investigated yet. For this reason the role of different realizations of the same estimation model needs to be further investigated

On the model-based monitoring of industrial batch crystallizers

Crystallization is an important separation process to obtain high value-added chemicals in crystalline form from liquid solution in pharmaceutical, food and fine chemical industries. As most of the particulate processes, the quality of the solid product is determined by its particle size distribution (PSD). The achievement of the desired quality targets of the fine crystalline products relies on an efficient online process monitoring for separation supervision and control. However, hardware analyzers able to online measure the PSD and the solute concentration are rarely available, due to their costs \cite{Multi}. These unmeasured process variables can be estimated by state estimators that combine information from the process model and secondary measurements. The problem of designing state observers for online monitoring the PSD evolution has been mostly addressed under the assumption that some PSD measurements were available (see \cite{Mesb} and literature therein), which is not likely in practice. This work proposes a methodology to asses the feasibility of using common measurements (e.g. temperature and liquid fraction) for estimation purposes based on local observability \cite{Herm} and detectability \cite{AlFer} arguments. The results are supported using a data-derived technique, with data generated by a simulation model of the industrial crystallizer. Based on the results of the observability analysis, the structure of a state estimator is proposed

Exercise training and detraining process affects plasma adiponectin level in healthy and spontaneously hypertensive rats

BACKGROUND: Adiponectin levels with long-term swimming exercise have been never investigated in spontaneously hypertensive rats (SHR). OBJECTIVE: This study was aimed to investigate the effects of exercise and detraining process on the adiponectin plasma levels of spontaneously hypertensive rats (SHR) and healthy Wistar-Kyoto rats (WKY). MATERIAL AND METHODS: The rats in the exercise groups were swimming for 10 weeks, 5 days/week, one hour in a day. The detraining rats were left to be sedentary in their cages for 5 weeks after 10 weeks of exercise period. RESULTS: The plasma adiponectin levels decreased in E and SHRE groups compared to the SC and the SHR groups, respectively. In addition, blood pressure was decreased in the exercise groups vs their controls. The adiponectin level was not found to be significantly different in ED and SHRED groups compared to their controls. The blood pressure did not differ between SDC and ED groups, although in the SHRED group it was found to be lower than in SHRSD group rats. CONCLUSION: The results of this study showed that exercise reduced plasma levels of adiponectin in healthy and spontaneously hypertensive rats. However, this difference disappeared at the end of the training processes. Our results suggest, that changes in plasma adiponectin levels are not responsible for changes in blood pressure

A comparative analysis of the cephalic microbiome: The ocular, aural, nasal/nasopharyngeal, oral and facial dermal niches

The human face/head supports a highly diverse population of microorganisms across a diverse range of microhabitats. This biogeographical diversity has given rise to selection pressure resulting in the formation of distinct bacterial communities between sites. This review investigates the similarity and differences of microbiomes across the different biogeographies of the human face and discusses a potential pathway for microbial circulation within individuals and within a population to maintain microbiome niches and diversity

Questioning impact: interconnection between extra-organizational resources and agency of equality and diversity officers

This paper examines the change agency of equality and diversity (E&D) officers with a specific emphasis on the role of extra-organizational influences and resources. The paper is informed by qualitative material collected through interviews with E&D officers from 20 higher education institutions in the UK. The paper offers an evidence-based analysis of the utility of extra-organizational mechanisms and intervention programmes for organizational E&D agenda and for the agentic influence of E&D officers. The paper contributes to both academic literature and policy-making. We present original empirical insights into the change agency of E&D officers by exploring the impact of extra-organizational bodies as potential mechanisms for support and influence. At the policy level, the paper provides evidence on the value of extra-organizational resources and tools that are produced by policy bodies in promoting progressive E&D agendas in organizations

Development of a task-level robot programming and simulation system

An ongoing project in developing a Task-Level Robot Programming and Simulation System (TARPS) is discussed. The objective of this approach is to design a generic TARPS that can be used in a variety of applications. Many robotic applications require off-line programming, and a TARPS is very useful in such applications. Task level programming is object centered in that the user specifies tasks to be performed instead of robot paths. Graphics simulation provides greater flexibility and also avoids costly machine setup and possible damage. A TARPS has three major modules: world model, task planner and task simulator. The system architecture, design issues and some preliminary results are given