Managing bank performance under COVID-19: A novel inverse DEA efficiency approach.

The evolution of the COVID-19 pandemic is highly unpredictable; however, its impacts are limited to neither a single sector nor a single country. This study evaluates the performance and efficiency of 49 Islamic banks across 10 countries during 2019-2020 to assess how those banks can preserve their performance and remain resilient in the aftermath of the COVID-19 pandemic. Using the conventional inverse data envelopment analysis (InvDEA) approach, we show that because of reductions in their outputs, 31 out of the 49 banks studied would need to reduce their inputs so that their efficiency can remain unchanged. However, we show that only 10 banks need to make such adjustments to maintain their efficiency levels using our proposed InvDEA efficiency model. The adjustment for those 10 banks would help in reducing more inputs, suggesting more cost savings, and improving the overall efficiency of the examined banks, compared with the other 31 banks.

European Right-Wing Populist Parties' Approach toward China: Between Anti-Globalization and Appeal for Diversified International Partnerships

Despite the fact that right-wing populist parties (RPPs) have gained increased prominence in the last 15 years in Europe and the amount of research regarding these parties has been on the rise, RPPs' attitude toward the People's Republic of China remains an understudied issue. The aim of this article is to examine questions that have not yet been thoroughly researched: how are the positions of right-wing populist parties (RPPs) on China shaped and how are they evolving, what causes such differing positions, and have there been any changes in the RPP's approach to China since the beginning of the COVID-19 pandemic? This article aims to answer these questions by analyzing the policies toward China of selected political parties (primarily, Hungary's Fidesz, Italy's Lega Nord, France's Rassemblement National but also parties such as the Polish Law and Justice, and Germany's Alternative für Deutschland). This article underlines that RPPs' policies on China are formulated based on what they perceive the development of international politics can do "for the people.” Their attitudes depend mainly on their stance toward free-market globalization, their need for alternatives in relations with the US and the EU, and their axiological perception of China. © 2019 The Author(s).

Efficiency of the Islamic Banking Sector: Evidence from Two-Stage DEA Double Frontiers Analysis

This paper examines the multi-dimensional efficiency of the Islamic banking sector and its determinants, including the impacts of the COVID-19 pandemic. To do that, we use a novel approach of two-stage data envelopment analysis (DEA) double frontiers to evaluate the overall efficiency of 79 Islamic banks across 16 countries (2005-2020). In the first-stage analysis, we found that the Islamic banking sector experienced an increasing trend in its efficiency and performance, even during the recent pandemic, although it varied across banks and countries. Our empirical results of the second-stage analysis further showed that economic development can help countries both withstand the recent pandemic and improve the efficiency and performance of their (Islamic) banking system. This, in turn, could help speed up the recovery process of the global economy. Since there is evidence that the Islamic banking sector is resilient to the COVID-19 pandemic, it is expected that this sector will be a driving force of such recovery.

The environment, social and governance (ESG) activities and profitability under COVID-19: evidence from the global banking sector

Purpose>This study investigated the impacts of the environment, social and governance (ESG) and its components on global bank profitability considering the COVID-19 outbreak.Design/methodology/approach>This study used a system generalized method of moments (GMM) proposed by Arellano and Bover (1995) to investigate the relationship between ESG and bank profitability using an unbalanced sample of 487 banks from 51 countries from 2006 to 2021.Findings>The findings generally found that ESG activities may reduce bank profitability, thus supporting the trade-off hypothesis that adopting ESG standards could increase bank costs while lowering profitability. In addition, there is a U-shaped relationship between ESG and bank profitability, suggesting that ESG activities can help improve bank performance in the long term. Such an effect is the first time observed in the global banking sector. This study’s results are robust across different models and settings (e.g., developed vs. developing countries, different levels of profitability, and samples with vs without US banks).Practical implications>This study provides empirical evidence to support many countries' sustainable development policies. It also provides empirical incentives for bank managers to be more ESG-oriented.Originality/value>This study provides a better understanding of the roles of ESG activity and its components in the global banking system, considering the recent crises.

Author Correction: Advancing COVID-19 diagnosis with privacy-preserving collaboration in artificial intelligence (Nature Machine Intelligence, (2021), 3, 12, (1081-1089), 10.1038/s42256-021-00421-z)

In the version of this article initially published, the first name of Chuansheng Zheng was misspelled as Chuangsheng. The error has been corrected in the HTML and PDF versions of the article. © The Author(s) 2022.

Advancing COVID-19 diagnosis with privacy-preserving collaboration in artificial intelligence

Artificial intelligence provides a promising solution for streamlining COVID-19 diagnoses;however, concerns surrounding security and trustworthiness impede the collection of large-scale representative medical data, posing a considerable challenge for training a well-generalized model in clinical practices. To address this, we launch the Unified CT-COVID AI Diagnostic Initiative (UCADI), where the artificial intelligence (AI) model can be distributedly trained and independently executed at each host institution under a federated learning framework without data sharing. Here we show that our federated learning framework model considerably outperformed all of the local models (with a test sensitivity/specificity of 0.973/0.951 in China and 0.730/0.942 in the United Kingdom), achieving comparable performance with a panel of professional radiologists. We further evaluated the model on the hold-out (collected from another two hospitals without the federated learning framework) and heterogeneous (acquired with contrast materials) data, provided visual explanations for decisions made by the model, and analysed the trade-offs between the model performance and the communication costs in the federated training process. Our study is based on 9,573 chest computed tomography scans from 3,336 patients collected from 23 hospitals located in China and the United Kingdom. Collectively, our work advanced the prospects of utilizing federated learning for privacy-preserving AI in digital health. The COVID-19 pandemic sparked the need for international collaboration in using clinical data for rapid development of diagnosis and treatment methods. But the sensitive nature of medical data requires special care and ideally potentially sensitive data would not leave the organization which collected it. Xiang Bai and colleagues present a privacy-preserving AI framework for CT-based COVID-19 diagnosis and demonstrate it on data from 23 hospitals in China and the United Kingdom.

DuCN: Dual-Children Network for Medical Diagnosis and Similar Case Recommendation Towards COVID-19

Early detection of the coronavirus disease 2019 (COVID-19) helps to treat patients timely and increase the cure rate, thus further suppressing the spread of the disease. In this study, we propose a novel deep learning based detection and similar case recommendation network to help control the epidemic. Our proposed network contains two stages: the first one is a lung region segmentation step and is used to exclude irrelevant factors, and the second is a detection and recommendation stage. Under this framework, in the second stage, we develop a dual-children network (DuCN) based on a pre-trained ResNet-18 to simultaneously realize the disease diagnosis and similar case recommendation. Besides, we employ triplet loss and intrapulmonary distance maps to assist the detection, which helps incorporate tiny differences between two images and is conducive to improving the diagnostic accuracy. For each confirmed COVID-19 case, we give similar cases to provide radiologists with diagnosis and treatment references. We conduct experiments on a large publicly available dataset (CC-CCII) and compare the proposed model with state-of-the-art COVID-19 detection methods. The results show that our proposed model achieves a promising clinical performance. © 2021, Springer Nature Switzerland AG.