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The purpose of this paper is to examine a solution strategy for coupled nonlinear magnetic-thermal problems and apply it to the heating process of a thin moving steel sheet. Performing efficient numerical simulations of induction heating processes becomes ever more important because of faster production development cycles, where the quasi steady-state solution of the problem plays a pivotal role.

To avoid time-consuming transient simulations, the eddy current problem is transformed into frequency domain and a harmonic balancing scheme is used to take into account the nonlinear BH-curve. The thermal problem is solved in steady-state domain, which is carried out by including a convective term to model the stationary heat transport due to the sheet velocity.

The presented solution strategy is compared to a classical nonlinear transient reference solution of the eddy current problem and shows good convergence, even for a small number of considered harmonics.

Numerical simulations of induction heating processes are necessary to fully understand certain phenomena, e.g. local overheating of areas in thin structures. With the presented approach it is possible to perform large 3D simulations without excessive computational resources by exploiting certain properties of the multiharmonic solution of the eddy current problem. Together with the use of nonconforming interfaces, the overall computational complexity of the problem can be decreased significantly.

The concept of productivity is central to performance management and decision-making, although it is complex and multifaceted. This paper aims to describe a methodology based on the use of Big Data in a cluster analysis combined with a data envelopment analysis (DEA) that provides accurate and reliable productivity measures in a large network of retailers.

The methodology is described using a case study of a leading kitchen furniture producer. More specifically, Big Data is used in a two-step analysis prior to the DEA to automatically cluster a large number of retailers into groups that are homogeneous in terms of structural and environmental factors and assess a within-the-group level of productivity of the retailers.

The proposed methodology helps reduce the heterogeneity among the units analysed, which is a major concern in DEA applications. The data-driven factorial and clustering technique allows for maximum within-group homogeneity and between-group heterogeneity by reducing subjective bias and dimensionality, which is embedded with the use of Big Data.

The use of Big Data in clustering applied to productivity analysis can provide managers with data-driven information about the structural and socio-economic characteristics of retailers' catchment areas, which is important in establishing potential productivity performance and optimizing resource allocation. The improved productivity indexes enable the setting of targets that are coherent with retailers' potential, which increases motivation and commitment.

This article proposes an innovative technique to enhance the accuracy of productivity measures through the use of Big Data clustering and DEA. To the best of the authors’ knowledge, no attempts have been made to benefit from the use of Big Data in the literature on retail store productivity.

The purpose of this paper is to present the result of calculations that were performed to estimate the structural weight of the passenger aircraft using novel technological solution. Mass penalty resulting from the installation of the fuselage boundary layer ingestion device was needed in the CENTRELINE project to be able to estimate the real benefits of the applied technology.

This paper focusses on the finite element analysis (FEA) of the fuselage and wing primary load-carrying structures. Masses obtained in these analyses were used as an input for the total structural mass calculation based on semi-empirical equations.

Combining FEA with semi-empirical equations makes it possible to estimate the mass of structures at an early technology readiness level and gives the possibility of obtaining more accurate results than those obtained using only empirical formulas. The applied methodology allows estimating the mass in case of using unusual structural solutions, which are not covered by formulas available in the literature.

Accurate structural mass estimation is possible at an earlier design stage of the project based on the presented methodology, which allows for easier and less costly changes in designed aircrafts.

The presented methodology is an original method of mass estimation based on a two-track approach. The analytical formulas available in the literature have worked well for aeroplanes of conventional design, but thanks to the connection with FEA presented in this paper, it is possible to estimate the structure mass of aeroplanes using unconventional technological solutions.

Might a country’s economic growth performance differ depending on the evolution of its human capital? This paper aims to consider education as a channel for human capital improvement and then for economic growth. The authors hypothesize the existence of a threshold for education, after which point the characteristics of economic growth change.

To address this question, the authors turn from a linear framework to a nonlinear one by applying smooth transition specifications.

This empirical analysis for Spain points to the existence of nonlinearities in the relationship between education and economic growth at country level, for both secondary and tertiary education. Next, as different patterns emerge in different regions, the authors provide a regional analysis for a number of representative Spanish regions. The results show that both secondary and tertiary education matter for economic growth and that nonlinearities in this relationship should be taken into account.

What is learnt from using Smooth Transition Regression models for the education-economic growth link is that the educational level of the population can be understood as a source of nonlinearities in the economic activity of a country (and of a region). Thus, depending on national and regional educational levels, economic growth behaves differently.

Although the importance of nonlinearities has been identified, linearity is usually assumed in this field of the literature. This paper calls into question the linearity assumption by using time series techniques for 1971-2013 in Spain, an OECD country, and testing whether the results at country level hold for different regions within Spain as a robustness check.

This study aims to use gray models to predict abnormal stock returns.

Data are collected from listed companies in the Tehran Stock Exchange during 2005-2015. The analyses portray three models, namely, the gray model, the nonlinear gray Bernoulli model and the Nash nonlinear gray Bernoulli model.

Results show that the Nash nonlinear gray Bernoulli model can predict abnormal stock returns that are defined by conditions other than gray models which predict increases, and then after checking regression models, the Bernoulli regression model is defined, which gives higher accuracy and fewer errors than the other two models.

The stock market is one of the most important markets, which is influenced by several factors. Thus, accurate and reliable techniques are necessary to help investors and consumers find detailed and exact ways to predict the stock market.

The purpose of this paper is to unpack the underlying mechanisms of entrepreneurs' passion, orientation and behavior by investigating the role of rational and nonrational cognitive elements. Building on dual process theory and sociointuitionism, a conceptual model is proposed in order to explore the relationship between entrepreneurial passion, entrepreneurial orientation (EO) and strategic entrepreneurship behavior (SEB). Specifically, entrepreneurs' linear thinking styles (System 2) and nonlinear thinking styles (System 1) are hypothesized as being significant moderators of such a relationship.

Covariance-based structural equation modeling (CB-SEM) is used to empirically validate the proposed conceptual model and test the moderating hypotheses on a sample of 300 entrepreneurs actively involved in European small and medium enterprises (SMEs).

Entrepreneurial passion is shown to be a significant antecedent of EO, which, in turn, strongly influences SEB. Moreover, entrepreneurs' linear thinking style positively moderates the EO-SEB relationship, but not the link between passion and EO. Instead, a nonlinear thinking style positively moderates the relationship between passion and EO, but not the links between EO and SEB.

Entrepreneurs should trust their nonlinear thinking style – related to affective/emotive and intuitive information processing systems – to foster the effect of their entrepreneurial passion on EO. Furthermore, entrepreneurs should rely on a linear thinking style, namely the rational and deliberative cognitive processes, to enhance the impact of their EO on SEB.

Dual process theory and sociointuitionism are integrated to simultaneously investigate the effect of nonrational and rational cognitive mechanisms on entrepreneurs' orientation and behavior. Moreover, the proposed model is empirically tested on a sample of entrepreneurs working in SMEs located in Europe, which have received little attention from entrepreneurship scholars in comparison to their US counterparts. The authors’ findings suggest important implications for entrepreneurs, policymakers and entrepreneurial universities educators.

Might the impact of the global economic policy uncertainty (GEPU) and the long-term bond yields on oil prices be asymmetric? This paper aims to consider the effects of the GEPU and the US long-term government bond yields on oil prices using quantile-based analysis and nonlinear vector autoregression (VAR) model. The author hypothesized whether the negative and positive changes in the GEPU and the long-term bond yields of the USA have different effects on oil prices.

To address this question, the author uses quantile cointegration model and the impulse response functions (IRFs) of the censored variable approach of Kilian and Vigfusson (2011).

The quantile cointegration test showed the existence of non-linear cointegration relationship, whereas Granger-causality analysis revealed that positive/negative variations in GEPU will have opposite effects on oil prices. This result was supported by the quantile regression model’s coefficients and nonlinear VAR model’s IRFs; more specifically, it was stressed that increasing/decreasing GEPU will deaccelerate/accelerate global economic activity and thus lead to a fall/rise in oil prices. On the other hand, the empirical models indicated that the impact of US 10-year government bond yields on oil prices is asymmetrical, while it was found that deterioration in the borrowing conditions in the USA may have an impact on oil prices by slowing down the global economic activity.

As a robustness check of the quantile-based analysis results, the slope-based Mork test is used.

The Polish economy has undergone major challenges and changes over the past few decades. The country's trade flows, in particular, have become more firmly tied to the country’s Western neighbors as they have grown in volume. This study examines Poland's trade balances in ten Standard International Trade Classification (SITC) sectors versus the United States of America, first testing for and isolating structural breaks in each time series. These breaks are then included in a set of the cointegration models to examine their macroeconomic determinants.

Linear and nonlinear and nonlinear autoregressive distributed lag models, both with and without dummies corresponding to structural breaks, are estimated.

One key finding is that incorporating these breaks reduces the significance of the real exchange rate in the model, supporting the hypothesis that this variable already incorporates important information. It also results in weaker evidence for cointegration of all variables in certain sectors.

This study looks only at one pair of countries, without any third-country effects.

An important country pair's trade relations is examined; in addition, the real exchange rate is shown to incorporate economic information that results in structural changes in the economy. The paper extends the existing literature by conducting an analysis of Poland's trade balances with the USA, which have not been studied in such a context so far. A strong point is a broad methodology that lets compare the results the authors obtained with different kinds of models, both linear and nonlinear ones, with and without structural breaks.

The purpose of this paper is to explore the impact of recent developments in corporate reporting, specifically from the carbon disclosure project (CDP) environment, in the evolution of European post-crisis financial markets.

Theoretical and instrumental advancements from nonlinear dynamics have been applied to the analysis of market behaviour and the online presence or reputation of major European listed banks.

The application of a nonlinear statistical methodology (i.e. the autoregressive fractionally integrated moving average [ARFIMA] estimation model) demonstrates the presence of a long history of collected data, thus indicating a certain degree of predictability in the time series. Also, this study confirms the existence of structural breakpoints, specifically the impact of the CDP reporting in both stock prices and online search trends of the sampled companies for certain periods.

This study introduces new methodological perspectives in corporate reporting studies, as the application of nonlinear techniques can be more effective in capturing corporate transparency issues. A limitation to overcome is to explore whether the impact of reporting is different due to the specific reporting behaviour each company adopts.

The “breakpoint” concept should enlighten the importance to firms of providing more information in specific moments, which can impact on both traditional (i.e. stock prices) and modern (i.e. online popularity) performance metrics. Additionally, it should be taken into account by stakeholders, when analysing the accountability of firms to improve their decision-making processes and policymakers, for monitoring and contrasting speculative and insider trading activities.

Online search trends represent a new public attitude to how society “measures” the effectiveness of firms’ disclosure behaviours.

Combining ARFIMA with structural break techniques can be regarded as a relevant and complementary addition to classic “market reaction” or “value relevance” techniques.

Striving to achieve the goal of carbon neutrality before 2060 indicates that China, as the most extensive power system in the world and a country based on coal power, is imperative to improve the technical level of electric power utilization. This paper aims to explore the nonlinear evolution mechanism of power technology progress under the constraints of net-zero carbon dioxide emissions in China.

This paper, first, based on China’s provincial panel data from 2000 to 2019, uses global direction distance function to measure power technological progress. Second, the threshold regression model is used to explore the nonlinear relationship between carbon emission reduction constraints on electric power technological progress.

There is a significant inverted U-shaped relationship between China’s provincial carbon emission reduction constraints and electric power technological progress. Meanwhile, the scale of regional economic development has a significant moderating effect on the relationship between carbon emission reduction constraints and power technological progress.

This paper puts forward targeted suggestions for perfecting regional carbon emission reduction policy and improving electric power technological progress.

Based on the global directional distance function, this paper extracts power as a production factor in total factor productivity and calculates the total factor electric power technological progress. This paper objectively reveals the influence mechanism of carbon emission reduction constraints on electric power technology progress based on the threshold regression model.