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This study investigates the role of market concentration and efficiency in banking system stability during the COVID-19 pandemic. We empirically test the hypothesis that market concentration and efficiency are significant determinants of bank performance and stability during the time of crises, using a sample of 575 banks in 20 countries in the Middle East and North Africa (MENA).

The main sources of bank data are the BankScope and BankFocus (Bureau van Dijk) databases, World Bank development indicators, and official websites of banks in MENA countries. This study combined descriptive and analytical approaches. We utilize a panel dataset and adopt panel data econometric techniques such as fixed/random effects and the Generalized Method of Moments (GMM) estimator.

The results reveal that market concentration negatively affects bank profitability, whereas improved efficiency further enhances bank performance and contributes to the banking sector’s overall stability. Furthermore, our analysis indicates that during the COVID-19 pandemic, bank stability strongly depended on the level of market concentration, but not on bank efficiency. However, more efficient banks are more profitable and stable if the banking institutions are Islamic. Similarly, Islamic banks with the same level of efficiency demonstrated better overall financial performance during the pandemic than their conventional peers did.

The main limitation is related to the period of COVID-19 pandemic that was covered in this paper (2020–2021). Therefore, further investigation of the COVID-19 effects on bank profitability and risk will require an extended period of the pandemic crisis, including 2022.

This study provides information that will enable bank managers and policymakers in MENA countries to assess the growing impact of market concentration and efficiency on the banking sector stability. It also helps them in formulating suitable strategies to mitigate the adverse consequences of the COVID-19 pandemic. Our recommendations are useful guides for policymakers and regulators in countries where Islamic and conventional banking systems co-exist and compete, based on different business models and risk management practices.

The authors contribute to the banking stability literature by investigating the role of market concentration and efficiency as the main determinants of bank performance and stability during the COVID-19 pandemic. This study is the first to analyze banking sector stability in the MENA region, using both individual and risk-adjusted aggregated performance measures.

The main purpose of this paper is to explore the impact of digital transformation on enterprises' performance considering financing constraints in the capital market to explore whether digital transformation improves enterprises' performance through the financing constraints channel.

This study, using a panel data set of 14,669 observations of 2,858 non-financial enterprises that issued A shares on the Shanghai and Shenzhen stock exchanges from 2013 to 2019, theoretically and empirically tests the impact and mechanism of digital transformation on enterprise performance.

Digital transformation has a significant positive effect on enterprise performance; this conclusion remains the same after the robustness test and endogeneity problems are dealt with. Financing constraints play a mediation role between digital transformation and enterprise innovation. The effect of digital transformation on enterprise performance varies significantly by size, ownership and industry.

The theoretical contributions of this study not only enrich the literature on the economic benefits and mechanism of digital transformation but also expand the literature on the factors that influence enterprise performance. The practical contribution of this study is the reference that it provides for implementing decisions about enterprise digital transformation and formulating differentiated policies for government digital transformation.

Social media networks make their services freely available to all users. Users pay for the service received with the time and attention taken by the advertisements. This chapter argues that social media platforms are a unique form of monopoly driven by “the more the merrier” effect (i.e., network effects) in users' consumption. These monopolies exercise market power, not by charging higher prices to users but by “tying” larger amounts of advertising to their content. Traditional antitrust instruments designed to address excessive pricing and reduced output by monopolies need to be reframed to tame the attention economy problems in the social media industry. This chapter discusses five antitrust instruments grouped in three categories: structural, behavioral, and market-based remedies. Market-based solutions are the least explored in the literature, despite being the most promising instruments to lower the attention costs imposed on users, while preserving the economies of scope in production and the network effects in consumption, and possibly maintaining free access to social media, as we know it today.

This study aims to predict the fracture properties of geopolymer concrete, which is necessary for studying failure behaviour of concrete.

Geopolymers are new alternative binders for cement in which polymerization gives strength to concrete rather than through hydration. Geopolymer concrete was developed from industrial byproducts such as GGBS and dolomite. Present study estimates the fracture energy of GGBS geopolymer concrete using three point bending test (RILEM TC50-FMC) with different percentages of dolomite and compare with cement concrete having same strength.

The fracture properties such as peak load, critical stress intensity factor, fracture energy and characteristic length are found to be higher for GGBS-dolomite geopolymer concrete, when their proportion becomes 70:30.

To the best of the authors’ knowledge, this is an original experimental work.

Histopathology biopsy imaging is currently the gold standard for the diagnosis of breast cancer in clinical practice. Pathologists examine the images at various magnifications to identify the type of tumor because if only one magnification is taken into account, the decision may not be accurate. This study explores the performance of transfer learning and late fusion to construct multi-scale ensembles that fuse different magnification-specific deep learning models for the binary classification of breast tumor slides.

Three pretrained deep learning techniques (DenseNet 201, MobileNet v2 and Inception v3) were used to classify breast tumor images over the four magnification factors of the Breast Cancer Histopathological Image Classification dataset (40×, 100×, 200× and 400×). To fuse the predictions of the models trained on different magnification factors, different aggregators were used, including weighted voting and seven meta-classifiers trained on slide predictions using class labels and the probabilities assigned to each class. The best cluster of the outperforming models was chosen using the Scott–Knott statistical test, and the top models were ranked using the Borda count voting system.

This study recommends the use of transfer learning and late fusion for histopathological breast cancer image classification by constructing multi-magnification ensembles because they perform better than models trained on each magnification separately.

The best multi-scale ensembles outperformed state-of-the-art integrated models and achieved an accuracy mean value of 98.82 per cent, precision of 98.46 per cent, recall of 100 per cent and F1-score of 99.20 per cent.