Role of institutional pressures and resources in the adoption of big data analytics powered artificial intelligence, sustainable manufacturing practices and circular economy capabilities

This study supplies contributions to the existing literature with a state-of-the-art bibliometric review of sustainable industrial and operation engineering as the field moves toward Industry 4.0, ...

Sustainable industrial and operation engineering trends and challenges Toward Industry 4.0: a data driven analysis

Internationalization, digitalization, and sustainability: Are SMEs ready? A survey on synergies and substituting effects among growth paths

Digital economy, technological innovation, and green economic efficiency—Empirical evidence from 277 cities in China

During the current global public health emergency caused by novel coronavirus disease 19 (COVID-19), researchers and medical experts started working day and night to search for new technologies to mitigate the COVID-19 pandemic. Recent studies have shown that artificial intelligence (AI) has been successfully employed in the health sector for various healthcare procedures. This study comprehensively reviewed the research and development on state-of-the-art applications of artificial intelligence for combating the COVID-19 pandemic. In the process of literature retrieval, the relevant literature from citation databases including ScienceDirect, Google Scholar, and Preprints from arXiv, medRxiv, and bioRxiv was selected. Recent advances in the field of AI-based technologies are critically reviewed and summarized. Various challenges associated with the use of these technologies are highlighted and based on updated studies and critical analysis, research gaps and future recommendations are identified and discussed. The comparison between various machine learning (ML) and deep learning (DL) methods, the dominant AI-based technique, mostly used ML and DL methods for COVID-19 detection, diagnosis, screening, classification, drug repurposing, prediction, and forecasting, and insights about where the current research is heading are highlighted. Recent research and development in the field of artificial intelligence has greatly improved the COVID-19 screening, diagnostics, and prediction and results in better scale-up, timely response, most reliable, and efficient outcomes, and sometimes outperforms humans in certain healthcare tasks. This review article will help researchers, healthcare institutes and organizations, government officials, and policymakers with new insights into how AI can control the COVID-19 pandemic and drive more research and studies for mitigating the COVID-19 outbreak.

Applications of artificial intelligence in COVID-19 pandemic: A comprehensive review

Influence of artificial intelligence on technological innovation: Evidence from the panel data of china's manufacturing sectors

This chapter develops a control-theory model for the movement of money within an economic system based on changes in purchasing power of a currency by analyzing how the purchasing power changes within the economic system, and it details with the problem of how our method of reducing the order of the original control-theory model can be utilized to obtain models of reduced order that resemble the original model while still processing such important properties as observability and controllability.

Order Reduction of Dynamic Monetary Systems

Recent financial events since the 1990s, such as the Southeast Asian financial crisis, the South American financial crisis, the US subprime mortgage crisis, etc., caused abnormal changes to the monetary systems of the affected countries, leading to economic recessions and losses of wealth (Rickards 2012). That helps to make people ask the following question: is there a new method that can effectively be employed to address problems related to the movement of money, while providing measures needed to counter potential financial crises facing various economies? And this chapter attempts to address this question by laying down the necessary foundation.

Systemic Representation of Economic Organizations

Intergovernmental collaboration is essential for regionally coordinated air pollution prevention and control. An evolutionary game model of local government behavior strategy selection taking into account the fixed cost of the local government, the total amount of network production factors, and the total amount of total network factors of production that can be moved is built based on the social capital theory in order to realize the cross-border collaborative control of regional air pollution. The issue of an intergovernmental cooperation framework for collaborative prevention and control of large-scale air pollution is addressed from the standpoint of the multi-stakeholder “benefit–cost” drive. Additionally, the major variables influencing the behavioral approach selection for intergovernmental cooperation are considered. The initial sensitivity of the evolution path of the local government behavioral strategy is also analyzed. The results of this study are: (1) The primary elements impacting intergovernmental cooperation on joint prevention and control of air pollution are fixed costs and fixed benefits, and reducing the fixed costs of such cooperation in an appropriate manner without compromising local governments’ pollution control can do so. (2) Under the assumption that local governments have fixed expenses, the total amount of network factors of production and total network factors of production that can be moved factors of production have a direct impact on intergovernmental cooperation. When local governments’ fixed costs are constant, they are more likely to choose the cooperative behavior option if the sum of their network production factors and total network factors of production that can be moved is higher. (3) The initial probability of cooperation among the three parties and the total amount of production factors have an impact on the system’s ESS when local governments in the area have equal total production factors. The study’s findings can offer theoretical justification for the “profit-driven” intergovernmental coordination of joint prevention and management of air pollution.

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Baohua Yang

Papers

Influence of artificial intelligence on technological innovation: Evidence from the panel data of china's manufacturing sectors

Order Reduction of Dynamic Monetary Systems

Systemic Representation of Economic Organizations

Evolutionary Game Mechanism of Governmental Cross-Regional Cooperation in AirPollution Management