A proposal to revive the European Fiscal Framework. Bruegel Policy Contribution ISSUE 2016/07 MARCH 2016

Pro-cyclical fiscal tightening might be one reason for the anaemic economic recovery in Europe, raising questions about the effectiveness of the EU’s fiscal framework in achieving its two main objectives: public debt sustainability and fiscal stabilisation. • In theory, the current EU fiscal rules, with cyclically adjusted targets, flexibility clauses and the option to enter an excessive deficit procedure, allow for large-scale fiscal stabilisation during a recession. However, implementation of the rules is hindered by the badly-measured structural balance indicator and incorrect forecasts, leading to erroneous policy recommendations. The large number of flexibility clauses makes the system opaque. • The current inefficient European fiscal framework should be replaced with a system based on rules that are more conducive to the two objectives, more transparent, easier to implement and which have a higher potential to be complied with. • The best option, re-designing the fiscal framework from scratch, is currently unrealistic. Therefore we propose to eliminate the structural balance rules and to introduce a new public expenditure rule with debt-correction feedback, embodied in a multi-annual framework, which would also support the central bank’s inflation target. A European Fiscal Council could oversee the system.

The behavioural homogeneity evaluation framework:Multi-level evaluations of consumer involvement in international segmentation

This paper presents a generic strategic framework of alternative international marketing strategies and market segmentation based on intra- and inter-cultural behavioural homogeneity. Consumer involvement (CI) is proposed as a pivotal construct to capture behavioural homogeneity, for the identification of market segments. Results from a five-country study demonstrate how the strategic framework can be valuable in managerial decision-making. First, there is evidence for the cultural invariance of the measurement of CI, allowing a true comparison of inter- and intra-cultural behavioural homogeneity. Second, CI influences purchase behaviour, and its evaluation provides a rich source of information for responsive market segmentation. Finally, a decomposition of behavioural variance suggests that national-cultural environment and nationally transcendent variables explain differences in behaviour. The Behavioural Homogeneity Evaluation Framework therefore suggests appropriate international marketing strategies, providing practical guidance for implementing involvement-contingent strategies. © 2007 Academy of International Business. All rights reserved.

An advanced real-time predictive maintenance framework for large scale machine systems

This thesis introduces and develops a novel real-time predictive maintenance system to estimate the machine system parameters using the motion current signature. Recently, motion current signature analysis has been addressed as an alternative to the use of sensors for monitoring internal faults of a motor. A maintenance system based upon the analysis of motion current signature avoids the need for the implementation and maintenance of expensive motion sensing technology. By developing nonlinear dynamical analysis for motion current signature, the research described in this thesis implements a novel real-time predictive maintenance system for current and future manufacturing machine systems. A crucial concept underpinning this project is that the motion current signature contains infor­mation relating to the machine system parameters and that this information can be extracted using nonlinear mapping techniques, such as neural networks. Towards this end, a proof of con­cept procedure is performed, which substantiates this concept. A simulation model, TuneLearn, is developed to simulate the large amount of training data required by the neural network ap­proach. Statistical validation and verification of the model is performed to ascertain confidence in the simulated motion current signature. Validation experiment concludes that, although, the simulation model generates a good macro-dynamical mapping of the motion current signature, it fails to accurately map the micro-dynamical structure due to the lack of knowledge regarding performance of higher order and nonlinear factors, such as backlash and compliance. Failure of the simulation model to determine the micro-dynamical structure suggests the pres­ence of nonlinearity in the motion current signature. This motivated us to perform surrogate data testing for nonlinearity in the motion current signature. Results confirm the presence of nonlinearity in the motion current signature, thereby, motivating the use of nonlinear tech­niques for further analysis. Outcomes of the experiment show that nonlinear noise reduction combined with the linear reverse algorithm offers precise machine system parameter estimation using the motion current signature for the implementation of the real-time predictive maintenance system. Finally, a linear reverse algorithm, BJEST, is developed and applied to the motion current signature to estimate the machine system parameters.

Formation and characterization of ultra-sensitive surface plasmon resonance sensor based upon a nano-scale corrugated multi-layered coated D-shaped optical fiber

We present experimental results on the performance of a series of coated, D-shaped optical fiber sensors that display high spectral sensitivities to external refractive index. Sensitivity to the chosen index regime and coupling of the fiber core mode to the surface plasmon resonance (SPR) is enhanced by using specific materials as part of a multi-layered coating. We present strong evidence that this effect is enhanced by post ultraviolet radiation of the lamellar coating that results in the formation of a nano-scale surface relief corrugation structure, which generates an index perturbation within the fiber core that in turn enhances the coupling. We have found reasonable agreement when we modeling the fiber device. It was found that the SPR devices operate in air with high coupling efficiency in excess of 40 dB with spectral sensitivities that outperform a typical long period grating, with one device yielding a wavelength spectral sensitivity of 12000 nm/RIU in the important aqueous index regime. The devices generate SPRs over a very large wavelength range, (visible to 2 mu m) by alternating the polarization state of the illuminating light.

The measurement properties of the menorrhagia multi-attribute quality-of-life scale:a psychometric analysis

Menorrhagia, or heavy menstrual bleeding (HMB), is a common gynaecological condition. As the aim of treatment is to improve women's wellbeing and quality of life (QoL), it is necessary to have effective ways to measure this. This study investigated the reliability and validity of the menorrhagia multi-attribute scale (MMAS), a menorrhagia-specific QoL instrument. Participants (n = 431) completed the MMAS and a battery of other tests as part of the baseline assessment of the ECLIPSE (Effectiveness and Cost-effectiveness of Levonorgestrel-containing Intrauterine system in Primary care against Standard trEatment for menorrhagia) trial. Analyses of their responses suggest that the MMAS has good measurement properties and is therefore an appropriate condition-specific instrument to measure the outcome of treatment for HMB. © 2011 The Authors BJOG An International Journal of Obstetrics and Gynaecology © 2011 RCOG.

Clustering web documents using hierarchical representation with multi-granularity

Web document cluster analysis plays an important role in information retrieval by organizing large amounts of documents into a small number of meaningful clusters. Traditional web document clustering is based on the Vector Space Model (VSM), which takes into account only two-level (document and term) knowledge granularity but ignores the bridging paragraph granularity. However, this two-level granularity may lead to unsatisfactory clustering results with “false correlation”. In order to deal with the problem, a Hierarchical Representation Model with Multi-granularity (HRMM), which consists of five-layer representation of data and a twophase clustering process is proposed based on granular computing and article structure theory. To deal with the zero-valued similarity problemresulted from the sparse term-paragraphmatrix, an ontology based strategy and a tolerance-rough-set based strategy are introduced into HRMM. By using granular computing, structural knowledge hidden in documents can be more efficiently and effectively captured in HRMM and thus web document clusters with higher quality can be generated. Extensive experiments show that HRMM, HRMM with tolerancerough-set strategy, and HRMM with ontology all outperform VSM and a representative non VSM-based algorithm, WFP, significantly in terms of the F-Score.