The Heterogeneity of Convergence in Transition Countries

For two groups of post-communist countries (CEE and CIS) we estimated the parameters of convergence equations on the basis of annual data. We depart from standard econometric theory, which involves panel regression techniques. We test cross-country heterogeneity of parameters within a system of Seemingly Unrelated Regression Equations (SURE). We show empirical evidence in favour of the variability of parameters describing the convergence effect and productivity growth rates across countries. Our approach seems a convincing alternative to the panel regression approach where random effects can be estimated, imposing an assumption about the constancy of structural parameters within the group of countries under analysis. We discuss the role of the global financial crisis in the heterogeneity of convergence processes and productivity at the country level. The aforementioned SURE model was estimated based on two datasets, one containing observations prior to the crisis and the second containing the whole sample.This research was financed by National Science Centre, Poland (decision DEC-2016/21/B/HS4/01565

4D Seismic History Matching Incorporating Unsupervised Learning

The work discussed and presented in this paper focuses on the history matching of reservoirs by integrating 4D seismic data into the inversion process using machine learning techniques. A new integrated scheme for the reconstruction of petrophysical properties with a modified Ensemble Smoother with Multiple Data Assimilation (ES-MDA) in a synthetic reservoir is proposed. The permeability field inside the reservoir is parametrised with an unsupervised learning approach, namely K-means with Singular Value Decomposition (K-SVD). This is combined with the Orthogonal Matching Pursuit (OMP) technique which is very typical for sparsity promoting regularisation schemes. Moreover, seismic attributes, in particular, acoustic impedance, are parametrised with the Discrete Cosine Transform (DCT). This novel combination of techniques from machine learning, sparsity regularisation, seismic imaging and history matching aims to address the ill-posedness of the inversion of historical production data efficiently using ES-MDA. In the numerical experiments provided, I demonstrate that these sparse representations of the petrophysical properties and the seismic attributes enables to obtain better production data matches to the true production data and to quantify the propagating waterfront better compared to more traditional methods that do not use comparable parametrisation techniques

Metamodel-based model conformance and multiview consistency checking

Model-driven development, using languages such as UML and BON, often makes use of multiple diagrams (e.g., class and sequence diagrams) when modeling systems. These diagrams, presenting different views of a system of interest, may be inconsistent. A metamodel provides a unifying framework in which to ensure and check consistency, while at the same time providing the means to distinguish between valid and invalid models, that is, conformance. Two formal specifications of the metamodel for an object-oriented modeling language are presented, and it is shown how to use these specifications for model conformance and multiview consistency checking. Comparisons are made in terms of completeness and the level of automation each provide for checking multiview consistency and model conformance. The lessons learned from applying formal techniques to the problems of metamodeling, model conformance, and multiview consistency checking are summarized

An introduction to RIVPACS

RIVPACS (River InVertebrate Prediction And Classification System) is a software package developed by the Institute of Freshwater Ecology (IFE). The primary application is to assess the biological quality of rivers within the UK. RIVPACS offers site-specific predictions of the macroinvertebrate fauna to be expected in the absence of major environmental stress. The expected fauna is derived by RIVPACS using a small suite of environmental characteristics. The biological evaluation is then obtained by comparing the fauna observed at the site with the expected fauna. RIVPACS also includes a site classification based on the macroinvertebrate fauna of the component reference sites. New sites, judged by their fauna to be of high biological quality, may be allocated to classification groups within the fixed RIVPACS classification. This has potential for evaluating sites for conservation. In this chapter, the origins and history of the RIVPACS approach are described, including major scientific and operational developments over the life of the project. RIVPACS III is described in detail and predictions at different taxonomic levels are demonstrated. The value of the reference dataset for river management and conservation is examined, and the chapter concludes with a brief consideration of some future challenges

Modelling mobile health systems: an application of augmented MDA for the extended healthcare enterprise

Mobile health systems can extend the enterprise computing system of the healthcare provider by bringing services to the patient any time and anywhere. We propose a model-driven design and development methodology for the development of the m-health components in such extended enterprise computing systems. The methodology applies a model-driven design and development approach augmented with formal validation and verification to address quality and correctness and to support model transformation. Recent work on modelling applications from the healthcare domain is reported. One objective of this work is to explore and elaborate the proposed methodology. At the University of Twente we are developing m-health systems based on Body Area Networks (BANs). One specialization of the generic BAN is the health BAN, which incorporates a set of devices and associated software components to provide some set of health-related services. A patient will have a personalized instance of the health BAN customized to their current set of needs. A health professional interacts with their\ud patients¿ BANs via a BAN Professional System. The set of deployed BANs are supported by a server. We refer to this distributed system as the BAN System. The BAN system extends the enterprise computing system of the healthcare provider. Development of such systems requires a sound software engineering approach and this is what we explore with the new methodology. The methodology is illustrated with reference to recent modelling activities targeted at real implementations. In the context of the Awareness project BAN implementations will be trialled in a number of clinical settings including epilepsy management and management of chronic pain