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This paper shows how informative qualitative research into student perceptions and values can be carried out by studying how small groups of students (focus groups) respond to a text (such as a literary text or newspaper article), within a ‘semi-structured’ framework. The discussions are prompted and structured by the researchers’ questions and prompts to elicit students’ attitudes and experience, but the structure is flexible enough to give space for unforeseen answers and questions. The paper explains the role of the researcher/moderator and research assistant, and gives some suggestions for conducting focus groups based on this technique.

The USA is one of the largest oil producers in the world. For this purpose, the authors model and predict the US conventional and unconventional oil production during the period 2000–2030.

In this research, the system dynamics (SD) model has been used. In this model, economic, technical, geopolitical, learning-by-doing and environmental (social costs of carbon) issues are considered.

The results of the simulation, after successfully passing the validation test, show that the US unconventional oil production rate under the optimistic scenario (high oil prices) in 2030 is about 12.62 million barrels/day (mb/day), under the medium oil price scenario is about 11.4 mb/day and under the pessimistic scenario (low oil price) is about 10.18 mb/day. The results of US conventional oil production forecasting under these three scenarios (high, medium and low oil prices) show oil production of 4.62, 4.26 and 3.91 mb/day, respectively.

The contribution of this study is important in several respects: First, by modeling SD that technical, economic, proven reserves and technology factors are considered, this paper models US conventional and unconventional oil production separately. In this modeling, nonlinear relationships and feedback loops are presented to better understand the relationships between variables. Second, given the importance of environmental issues, the modeling of social costs of CO₂ emissions per barrel of oil is also presented and considered as a part of oil production costs. Third, conventional and unconventional US oil production by 2030 is forecast separately, the results of this study could help policymakers to develop unconventional oil and plan for energy self-sufficiency.

The purpose of this study is to investigate and analysis of energy consumption for this industry. The core part of any energy management system (EnMS) in industry is to perfectly monitor the energy consumption of significant users and to continuously improve the energy performance. In petrochemical plants, production deals with energy-intensive processes, and measuring energy performance for recognition and assessment of potentials for saving is critical.

The required data are exploited for the period of March 2011-August 2016 (data set: 2,012 days). Multivariate linear regression (MLR) and multi-layer perceptron artificial neural network (ANN) methods are separately used to anticipate the energy consumption. The baseline will be assumed as a reference to be compared with the actual data to estimate the real saving values. Finally, cumulative summations (CUSUM) are proposed and applied as an effective indicator for measurement of energy performance in an LDPE.

In this study, two statistical methods of MLR and ANN were used to design and develop a comprehensive energy baseline representing the predicted amounts of energy consumption based on the recognized drivers. Although both models imply robust outcomes, when the relative errors are taken into account, performance of ANN models appears fairly superior compared to the MLR model.

It is highly suggested to the ISO technical committee dealing with energy management standards, to consider the proposed model for baseline development in the future version of the standard ISO 50006 as the supplementary extension for the ISO 50001 for measuring energy performance using EnB and EnPI. As for future studies, the research can be extended to investigate the uncertainty and the model could also become completed applying more advanced ANNs such as recurrent neural networks.

This study applies VAR and ANN techniques to make ex-post forecast of U.S. oil price movements. The VAR-based forecast uses three endogenous variables: lagged oil price, lagged oil supply and lagged energy consumption. However, the VAR model suggests that the impacts of oil supply and energy consumption has limited impacts on oil price movement. The forecast of the genetic algorithm-based ANN model is made by using oil supply, energy consumption, and money supply (M1). Root mean squared error and mean absolute error have been used as the evaluation criteria. Our analysis suggests that the BPN-GA model noticeably outperforms the VAR model.

Possibly the largest user of industrial gold plating is the electronics industry, which demands plating of the highest quality and reliability. In order to ensure these vital requirements considerable research and development work has been undertaken in the past and is still in progress. In this article the author surveys some of the latest techniques resulting from these investigations.

The purpose of this paper is to propose the development of return forecasting model for mudharabah time deposit product in Islamic bank based on artificial neural networks (ANNs).

The analysis consists of two main elements. First element is the identification and selection of significant macroeconomic variables that determine return volatility of mudharabah time deposit in Indonesian Islamic bank industry. Second element is the implementation of appropriate ANNs model according to neural networks properties, and model evaluation based on simulated return predictions of mudharabah time deposit product in Bank Syariah Mandiri (RR‐BSM).

It is shown that monthly changes of return can be predicted quite well. The model provides a satisfactory result in forecasting RR‐BSM for 12 months ahead with 95.22 per cent accuracy. These results suggest that the ANNs can be applied as an adequate tool to help depositors in predicting future return of mudharabah time deposit product.

There is believed to be no other empirical study of Islamic banks that exclusively examines the utilization of ANNs to forecast time deposit return as well as return from other investment instruments.

The purpose of this paper is to simulate the function of foreign trade, foreign direct investment (FDI) and regional gross domestic product (GDP) in China, explore how these two variables affect regional GDP together and provide evidence to export‐led growth (ELG) and FDI‐led growth.

Artificial neural network (ANN) is introduced in the model. This nonlinear and adaptive computation obtains a three‐dimension function that is different from linear models.

New evidence was found for ELG and FDI‐led growth with data of 28 regions in China in the period of 1994‐2005. The simulation reveals that with foreign trade and FDI scale varying, marginal GDP in different Chinese regions is positive. Because of the nonlinear system, a wave pattern of marginal GDP was found and an optimal scale of foreign trade and FDI for Chinese regions. Results in the simulation also indicate the possibility of economic deconcentration in some Chinese regions.

New evidence is provided for ELG and FDI‐led growth. Different from conventional methods, ANN model as a nonlinear system is introduced in the study in which optimal scale of foreign trade and FDI for Chinese regions is obtained.

This research aims to describe the influence of weight per cent of graphite (Gr), applied load and sliding speed on the wear behavior of aluminum (Al) alloy A356 reinforced with silicon carbide (SiC) (10 Wt.%) and Gr (1 Wt.% and 5 Wt.%) particles. The objective is to analyze the effect of the aforementioned parameters on a specific wear rate.

These hybrid composites are obtained by means of the compo-casting process. Tribological analyses were conducted on block-on-disc tribometer at three different loads (10, 20 and 30 N) and three different sliding speeds (0.25, 0.5 and 1 m/s), at the sliding distance of 900 m, in dry sliding wear conditions. Optimization of the tribological behavior was conducted via the Taguchi method, and ANOVA was used for the analysis of the specific wear rate. Confirmation tests are used to foresee and check the experimental results. Examined samples were analyzed via a scanning electron microscope (SEM). Regression models for predicting specific wear rate were developed with Taguchi and ANN (artificial neural network) methods.

The biggest impact on value of specific wear rate has the load (43.006%), while the impact of Wt.% Gr (31.514%) was less. After comparison of the results, i.e. regression models, for predicting the specific wear rate, it was observed that ANN was more efficient than the Taguchi method. The specific wear rate of Al alloy A356 with SiC (10 Wt.%) and Gr (1 Wt.% and 5 Wt.%) decreases with a decrease in the load and weight per cent of Gr-reinforcing material, as well as with a decrease in sliding speed.

The results obtained in this paper using the Taguchi method and the ANN method are useful for improving and further investigating the wear behavior of the SiC- and Gr-reinforced Al alloy A356.

Aim of the present monograph is the economic analysis of the role of MNEs regarding globalisation and digital economy and in parallel there is a reference and examination of some legal aspects concerning MNEs, cyberspace and e‐commerce as the means of expression of the digital economy. The whole effort of the author is focused on the examination of various aspects of MNEs and their impact upon globalisation and vice versa and how and if we are moving towards a global digital economy.