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The aim of this chapter is to examine the nominal and real convergence of Czechia. The importance of the convergence of Czechia with the euro area is linked to the future intention of joining the Economic and Monetary Union after the Maastricht criteria are met. This chapter covers the period from 2004 to 2021. We argue that nominal convergence is relative to the Maastricht criteria, when real convergence focuses on different areas: the Maastricht criteria, gross domestic product (GDP) per capita in purchasing power standards and real GDP growth rate, labour market (minimum labour costs and unemployment rates. Findings suggest that Czechia has reported the strongest real convergence in the area of relative economic level, moderate convergence of labour costs and divergence of unemployment. The nominal convergence analysis suggests that Czechia will not meet the Maastricht benchmarks in the near future and is not ready to join the euro area given its high inflation rate and the state of public finances.

This paper discusses the nominal and real convergence regarding Greece being a country-member of the European Union (EU), and of the Economic and Monetary Union (EMU). We argued that nominal convergence is relative to Maastricht criteria when real convergence has been investigated through six different axes: (1) the five Maastricht Criteria, (2) the GDP per capita in PPP prices, (3) the real GDP growth rates, (4) the minimum wages, (5) the HDI index development, and (6) the unemployment rates. We concluded for the case of Greece that by utilizing alternative indicators, such as the Maastricht criteria, and the above criteria only nominal convergence exists while real convergence appears to be a long-term target with many obstacles. In particular, Greece has managed to achieve the criteria proposed by the EMU (Maastricht Criteria) for membership, decisively different levels of unemployment, wages, and GDP growth rate/GDP per capita in PPP prices, and different human development indexes appear for the case of Greece.

Gulf Cooperation Council (GCC) was set up in 1981 between Bahrain, Oman, Qatar, Saudi Arabia, United Arab Emirates and Kuwait for strengthening cooperation and economic development in the region. The GCC has made strides towards economic consolidation by forming a customs union and a common market. The long-term vision is to create an Economic and Monetary Union (EMU) with a single currency. Progress towards the EMU has been slow and the recent oil price plunge has led to concerns regarding sustainable growth of member countries due to their significant dependence on oil and lack of diversification. The purpose of this paper is to analyse the scope of an EMU in the GCC against the backdrop of current oil crisis and examine sustainability of such a union. The paper studies convergence criteria similar to the ones followed by the accession countries of the European EMU in the 1990s preceding the introduction of the single currency Euro.

The paper draws its practical approach from the experience of the European Monetary Union, though the original idea of the single currency in Optimum Currency Areas was conceived by Mundell (1961). The present paper analyses macroeconomic time-series variables (e.g. GDP, budget deficits, debt, growth rates, inflation rates, exchange rates) for GCC during the period 2005-2014. Data has been sourced from United Nations Conference on Trade and Development (UNCTAD), The World Bank and International Monetary Fund (IMF) databases to study the convergence criteria adopted by the EMU countries for the introduction of the Euro.

The paper concludes that GCC economies are similar in terms of their structural and economic fundamentals. Most elements of the convergence criteria that were followed by the accession countries in Europe are fulfilled by the GCC member states, particularly during 2011-2014. The GCC states look similar in terms of sustainable growth, price stability and exchange rate stability – three aspects of convergence met by the European Union states. However, heavy dependence on oil and lack of diversification from oil and hydrocarbon-related products in the gross domestic product (GDP) composition of GCC states pose severe risks to the potential union. Fiscal vulnerabilities of these economies to oil price shocks, such as the current oil price crisis, create concerns for such a union during oil price lows. Widely divergent fiscal deficit-to-GDP ratios and rising debt-to-GDP ratios during periods of low oil prices imply the lack of sound and unsustainable public finances for some of the GCC states. The divergence has stemmed from widely different break-even oil prices for government budgets within the GCC and also due to varying degrees of oil dependencies between the member states. The scope of a successful and more sustainable EMU can be further explored once the GCC economics have achieved adequate diversity from oil.

The study is useful to policy makers, central banks, businesses and researchers since it highlights the EMU as a feasible option for the GCC states. The sustainability of the EMU is contingent on diversification of these economies in the future from oil and oil-related products. The study can be utilized by policy makers as a strategy to further restructure GCC economies towards greater resilience and integration prior to accession to the GCC EMU.

The purpose of this paper is to present a modified version of the non-dominated sorted genetic algorithm with an application in the design optimization of a power inductor for magneto-fluid hyperthermia (MFH).

The proposed evolutionary algorithm is a modified version of migration-non-dominated sorting genetic algorithms (M-NSGA) that now includes the self-adaption of migration events- non-dominated sorting genetic algorithms (SA-M-NSGA). Moreover, a criterion based on the evolution of the approximated Pareto front has been activated for the automatic stop of the computation. Numerical experiments have been based on both an analytical benchmark and a real-life case study; the latter, which deals with the design of a class of power inductors for tests of MFH, is characterized by finite element analysis of the magnetic field.

The SA-M-NSGA substantially varies the genetic heritage of the population during the optimization process and allows for a faster convergence.

The proposed SA-M-NSGA is able to find a wider Pareto front with a computational effort comparable to a standard NSGA-II implementation.

This chapter argues that the key Eurozone imbalances are not a failure of nation states. At the heart of the integration process is the convergence criteria – limits on government deficit, debt, interest rate, inflation, etc. While these were intended to eliminate asymmetries across countries, the conception of convergence was too narrow since the euro designers completely ignored the elephant in the room – that countries were on different technological frontiers. I show that this difference is an important determinant of the key macroeconomic imbalances across the Eurozone. It follows that the primary convergence criterion should be limits on non-price competitive gaps across countries. The chapter overturns the simplistic view of price competitiveness and illustrate that the regulating forces of competition originate from productive structures.

The purpose of this paper is to formulate, develop and test a reliability assessment model (GenRel) based on genetic algorithms.

Using genetic algorithm based modelling technique, a computer model was developed to predict mine equipment failures from historical data. Two different approaches in application of this technique are demonstrated.

A case study representing a test for convergence of the model was successfully performed. This is an indicator that GenRel can be used to predict equipment failures using a genetic algorithm based modeling technique.

The use of classical statistical techniques has proven to be an effective tool for reliability analysis of mining equipment. This paper presents an efficient alternative to these classical probability based reliability analysis methods. GenRel is a software solution which performs predictive reliability based upon genetic algorithms (GAs). The advantage of using this technique is the fact that the assumptions based on GAs are much simpler compared to classical statistical methods. The computer model is developed to accept a variety of user input data, most importantly, the ability to use real life historical data in the form of Time Between Failures (TBFs) or Time To Repair (TTRs).

The proposed research offers an alternative method to conventional statistically based reliability analysis and may lead to the foundation of a new approach for reliability assessment with potential applications in other industrial fields as well.

The aim of this chapter is to explore whether price dynamics is homogenous across Emerging Europe. We employ dynamic panel estimation techniques (including the Pooled Mean Group estimator of Pesaran, Smith, and Shin) over the 2003–2013 period and use Germany, and respectively, the European Union (EU) as the references. Results highlight some heterogeneity across the Emerging Europe members in terms of price convergence speed. Findings are robust across different specifications.

Surrogate models are extensively used to substitute real models which are expensive to evaluate in the time-dependent reliability analysis. Normally, different surrogate models have different scopes of application. However, information is often insufficient for analysts to select the most appropriate surrogate model for a specific application. Thus, the result precited by individual surrogate model tends to be suboptimal or even inaccurate. Ensemble model can effectively deal with the above concern. This work aims to study the application of ensemble model for reliability analysis of time-independent problems.

In this work, a method of reliability analysis for time-dependent problems based on ensemble learning of surrogate models is developed. The ensemble of surrogate models includes Kriging, radial basis function, and support vector machine. The prediction is approximated by the weighted average model. The ensemble learning of surrogate models is updated by finding and adding the sample points with large prediction errors throughout the entire procedure.

The effectiveness of the proposed method is verified by several examples. The results show that the ensemble of surrogate models can effectively propagate the uncertainty of time-varying problems, and evaluate the reliability with high prediction accuracy and computational efficiency.

This work proposes an adaptive learning framework for the uncertainty propagation of time-dependent problems based on the ensemble of surrogate models. Compared with individual surrogate models, the ensemble model not only saves the effort of selecting an appropriate surrogate model especially when the knowledge of unknown problem is lacking, but also improves the prediction accuracy and computational efficiency.