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The main research target of this paper is to capture the network effects using the case of mobile cellular telephony, identified in European telecommunication markets, and its determinants enhancing the process of digital technologies diffusion.

This research relies on panel and dynamic panel regression analysis. The empirical sample covers 30 European countries, and the period for the analysis is set for 2000–2019.

This work contributes by examining the network effects identified on European telecommunication markets that drive the process of digital technologies diffusion, but it also extends the understanding of the latter by tracing major determinants of fast network expansion, e.g. prices of access to and use of, per capita income, urbanisation, population density, accessibility of fixed telephony infrastructure. The main findings support the initial supposition that the installed base strongly enhances diffusion of new technologies, while other factors, for example, prices, are not of prime importance.

This research has certain managerial implications. The unveiled network effects driving adoption of technological innovations constitute a significant determinant of implementation of differentiation strategy by telecommunication companies. Due to network effects consumers' propensity to join the network is valued higher than the prices of services offered, which is crucial not only from the perspective of the company's pricing strategy but also enables telecommunication companies to introduce to the market new products and/or services concentrating on increasing its quality and usability rather than future prices.

This is the first work that empirically verifies the intercompanies and interpersonal diffusion of cellular telephony, hypothesising that this process relies on unique network effects.

The purpose of this paper is to investigate the performance of Passive Direct Methanol Fuel Cell (PDMFC) experimentally using various Membrane Electrode Assembly (MEA) shapes such as square, rectangle, rhombus, and circle with equal areas and equal perimeters. The variation in MEA shape/size is achieved by altering gasket openings in the dynamic regions.

In the equal areas of MEA shapes, gasket opening areas of 1963.5 (+/−0.2) mm2 are used. Whereas in the equal perimeters of shapes, gasket opening perimeters of 157.1 (+/−0.2) mm are used. In this experimentation, Nickel-201 current collectors with 45.3% of circular openings are used on both the anode and cathode sides. The experiment is carried out at a 5 molar methanol concentration to find out the highest power density of the cell.

In the equal areas, among the shapes that are chosen for investigation, the square shape opening consisting of a perimeter of 177.2 mm has developed a maximum power density of 6.344 mWcm⁻² and a maximum current density of 65.2 mAcm⁻². Similarly, in equal perimeters, the rhombus shape opening with an area of 1400 mm2 has developed a maximum power density of 7.714 mWcm⁻² and a maximum current density of 85.3 mAcm⁻².

The novelty of this research work is instead of fabricating various shapes and sizes of highly expensive MEAs, the desired shapes and sizes of the MEA are achieved by altering gasket openings over dynamic regions to find out the highest power density of the cell.

Introduction: Numerous decision-making situations are faced in education where Artificial Intelligence may be prevalent as a decision-making support tool to capture streams of learners’ behaviours.

Purpose: The purpose of the present study is to understand the role of AI in student performance assessment and explore the future role of AI in educational performance assessment.

Scope: The study tries to understand the adaptability of AI in the education sector for supporting the educator in automating assessment. It supports the educator to concentrate on core teaching-learning activities.

Objectives: To understand the AI adaption for educational assessment, the positives and negatives of confidential data collections, and challenges for implementation from the view of various stakeholders.

Methodology: The study is conceptual, and information has been collected from sources comprised of expert interactions, research publications, survey and Industry reports.

Findings: The use of AI in student performance assessment has helped in early predictions for the activities to be adopted by educators. Results of AI evaluations give the data that may be combined and understood to create visuals.

Research Implications: AI-based analytics helps in fast decision-making and adapting the teaching curriculum’s fast-changing industry needs. Students’ abilities, such as participation and resilience, and qualities, such as confidence and drive, may be appraised using AI assessment systems.

Theoretical Implication: Artificial intelligence-based evaluation gives instructors, students, and parents a continuous opinion on how students learn, the help they require, and their progress towards their learning objectives.

With the rapid development of the financial market, stock index futures have been the one of important financial instruments. Predicting stock index futures accurately can bring considerable benefits for investors. However, traditional models do not perform well in stock index futures forecasting. This study put forward a novel hybrid model to improve the predictive accuracy of stock index futures.

This study put forward a multivariate deep learning framework based on extreme gradient boosting (XGBoost) for stock index futures price forecasting. First, the original sequences were decomposed into several sub-sequences by variational mode decomposition (VMD), and these sub-sequences were reconstructed by sample entropy (SE). Second, the gradient boosting decision tree (GBDT) was used to rank the feature importance of influential factors, and the top influential factors were chosen for further prediction. Next, reconstructed sequence and the multiple factors screened were input into the bidirectional gate recurring unit (BiGRU) for modeling. Finally, XGBoost was used to integrate the modeling results.

For the sake of examining the robustness of the proposed model, CSI 500 stock index futures, NASDAQ 100 index futures, FTSE 100 index futures and CAC 40 index futures are selected as sample data. The empirical consequences demonstrate that the proposed model can serve as an effective tool for stock index futures prediction. In other words, the proposed model can improve the accuracy of stock index futures.

In this paper, an innovative hybrid model is proposed to enhance the predictive accuracy of stock index futures. Meanwhile, this method can be applied in other financial products prediction to achieve better forecasting results.

The purpose of this paper is to present a weakly supervised learning method to perform health evaluation and predict the remaining useful life (RUL) of rolling bearings.

Based on the principle that bearing health degrades with the increase of service time, a weak label qualitative pairing comparison dataset for bearing health is extracted from the original time series monitoring data of bearing. A bearing health indicator (HI) quantitative evaluation model is obtained by training the delicately designed neural network structure with bearing qualitative comparison data between different health statuses. The remaining useful life is then predicted using the bearing health evaluation model and the degradation tolerance threshold. To validate the feasibility, efficiency and superiority of the proposed method, comparison experiments are designed and carried out on a widely used bearing dataset.

The method achieves the transformation of bearing health from qualitative comparison to quantitative evaluation via a learning algorithm, which is promising in industrial equipment health evaluation and prediction.

The method achieves the transformation of bearing health from qualitative comparison to quantitative evaluation via a learning algorithm, which is promising in industrial equipment health evaluation and prediction.

This paper aims to propose an improved version of portfolio optimization model through the prediction of the future behavior of stock returns using a combined wavelet-based long short-term memory (LSTM).

First, data are gathered and divided into two parts, namely, “past data” and “real data.” In the second stage, the wavelet transform is proposed to decompose the stock closing price time series into a set of coefficients. The derived coefficients are taken as an input to the LSTM model to predict the stock closing price time series and the “future data” is created. In the third stage, the mean-variance portfolio optimization problem (MVPOP) has iteratively been run using the “past,” “future” and “real” data sets. The epsilon-constraint method is adapted to generate the Pareto front for all three runes of MVPOP.

The real daily stock closing price time series of six stocks from the FTSE 100 between January 1, 2000, and December 30, 2020, is used to check the applicability and efficacy of the proposed approach. The comparisons of “future,” “past” and “real” Pareto fronts showed that the “future” Pareto front is closer to the “real” Pareto front. This demonstrates the efficacy and applicability of proposed approach.

Most of the classic Markowitz-based portfolio optimization models used past information to estimate the associated parameters of the stocks. This study revealed that the prediction of the future behavior of stock returns using a combined wavelet-based LSTM improved the performance of the portfolio.

This study aims to propose and evaluate the progress in the basic-pixel (a strategy to generate continuous trajectories that fill out the entire surface) algorithm towards performance gain. The objective is also to investigate the operational efficiency and effectiveness of an enhanced version compared with conventional strategies.

For the first objective, the proposed methodology is to apply the improvements proposed in the basic-pixel strategy, test it on three demonstrative parts and statistically evaluate the performance using the distance trajectory criterion. For the second objective, the enhanced-pixel strategy is compared with conventional strategies in terms of trajectory distance, build time and the number of arcs starts and stops (operational efficiency) and targeting the nominal geometry of a part (operational effectiveness).

The results showed that the improvements proposed to the basic-pixel strategy could generate continuous trajectories with shorter distances and comparable building times (operational efficiency). Regarding operational effectiveness, the parts built by the enhanced-pixel strategy presented lower dimensional deviation than the other strategies studied. Therefore, the enhanced-pixel strategy appears to be a good candidate for building more complex printable parts and delivering operational efficiency and effectiveness.

This paper presents an evolution of the basic-pixel strategy (a space-filling strategy) with the introduction of new elements in the algorithm and proves the improvement of the strategy’s performance with this. An interesting comparison is also presented in terms of operational efficiency and effectiveness between the enhanced-pixel strategy and conventional strategies.

The current study aims to investigate the determinants of nonperforming loans (NPLs) in the GCC economies during the period spanning 2000 to 2018. It also examines whether the worldwide financial crisis of 2007–2008, which brought the issue of non–performing loans to the greater attention of academics and policymakers, had a substantial impact on NPLs in this region.

The sample consists of 53 conventional banks from GCC countries, and the basic data for the study is obtained from various sources such as Bankscope, IMF World Economic Outlook, World Bank and Chicago Board of Options Exchange Market Volatility Index. The estimations were done by dynamic panel data regression modeling using system generalized methods of moments.

The findings reveal that both, the non-oil real GDP growth rate and inflation have favorable effects on NPLs. On the other hand, domestic credit to the private sector and the volatility index have an adverse effect on NPLs. Furthermore, the period-wise analysis shows that the relevance and significance of the determinants of NPLs vary between the precrisis and postcrisis periods. It is also reflected through the intercept dummy, which is found to be significant, indicating that the financial crisis, as a global economic factor, had a significant impact on NPLs. A number of robustness tests are applied, which indicate that the results are mostly robust and consistent in terms of the significance of the explanatory variables and the direction of their relationship with the dependent variable.

Policymakers and bank authorities must strive to maintain a healthy economy and implement macroprudential policies to improve the financial stability of banks and reduce credit risk.

To the best of the authors’ knowledge, this is likely the first study that empirically investigates the influence of the financial crisis on NPLs in the context of GCC economies. In addition, the research spans 19 years to produce more conclusive results.

The purpose of this study is to analyse, firstly, whether education, financial inclusion, financial literacy and financial planning can be antecedents that affect Islamic welfare and, secondly, whether productivity can be a mediator to improve Islamic welfare.

This study involved quantitative research using data obtained from a survey. The respondents were 538 Muslim families in East Java, Indonesia. Structural equation modelling was used for the analysis.

This study tested 13 hypotheses, of which 10 were accepted. The accepted hypotheses refer to the effects of financial literacy on productivity, financial inclusion on productivity, financial planning on productivity, financial planning on Islamic welfare, education on Islamic welfare, productivity on Islamic welfare, financial literacy and productivity on Islamic welfare, financial inclusion and productivity on Islamic welfare and financial planning and productivity on Islamic welfare, as well as the effects of financial inclusion on Islamic welfare. Meanwhile, three hypotheses were not accepted; they refer to the effects of financial literacy on Islamic welfare, the effect of education on productivity, as well as the impact of education and productivity on Islamic welfare.

The study was conducted only with respondents living in East Java, so the results depict the condition of Muslim families’ welfare in East Java.

Research into the antecedents of Islamic welfare has received little academic attention, so this study explores how education, financial inclusion, financial literacy, financial planning and productivity could affect Islamic welfare among Muslim families.